{
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DD9Pv91lYWqSOgZiUTuEJIZBECU6xlp4uGre4EtK6BOPQJOOwnWQwydpwawJcMlIplKZoAnVKCjWIsFKvcirULOgc2/sdCnJiXameUbtilT6vLt6V2YTEYyQqVWKKdF0mnVn7dguFJjKxZgahwYtS+hz50BBJ7ag0Wm7JWZ7Xs5UncQie4Xdg6kZN3KmnGUBbt02+8Ay/95nN6LMq0/HnWe7zTEtPohAzdD7Bn8YoVwCYidgNn/IqMvcmTL45FddaZF4yWC5x/N/2DUsCdQgB5/JCFFPWWA6PEYloHvvm0JYHk1LAqyDiUDe9TAG8n5rfhmdhqcPi4mKLs6SptTcJ4zvQSCmOyDgKp5AyazqkxIlTJxltbPLnf/7nvP71r2d+fv4pn8azlWehQGIODc7IOO9jaqZGtMUHEkawhDiPUOeH10YU1BTEPSkUJ2JouwomaynpBsFDavJqNyRRIvzpfXfyox/9K77/spv55Xv+FnHKL33ZV/L6q2/k537uF7jx+pvYGG2yMDfH/YcO8ep//ipOnVrh5OnT7N65k3379jE3N8df/MW7WV7egfce7z1FUXDD9Tdwx+23cfnlB3MoMiXmF5doolF535rd2YWixQX+KQrjvHzuxcwmpMFJdMwZmjKZ0FJ+5xnCdhlDay2pJO1CIoJKA8nhFBozwmCTY0cOI87hxVOnIYUWoB5QVHOAQb1kFSQepwmhyNQHB2LZ9XJaYlaDh9hG6sS1VoyCK3tUlafb70FyPPrwI8T4ZB7Ec2GRPAsXxj2JDBQtTCaPGnjx2bRrgUw3CWc5IkrRruiWhCRpig+0oGJsA2xjbBADjxAjIJHGQSGKNCPef+QxHj9+ksHzhrxocSebhYJ3rG4OWF8/ze6dezhy4jDeldx4/fXceefd3P6pW7lg7wFWT5/k5NGTXHjphdxzz31U1cMsLvbRouTRhx4khMDb3/4bvPDmlzCqN7j11k/yP7/lLSwsbgdLTGypFmB7quzc8/KFIyIyISpqasO9sQUyo7SJm3GCX2U3LLsnakYyh1mD04poDaDZghq7eilRyxCNjiY2OImZimCaCV8Iq6nGm7REwSJDBim2oH22ZJx4ogW8LxHJoeXxQl4UBVVVsTkc4kQYbg6e5BKP7/WfKs/KAhFcRoxbQoyqJ8ZIDII5Mhcg1WDCMCodLXlic4NHTx/nBTt301hANYfTNMk0Ti5ZwThLLa6gJEu45IlmOKcUCiEKxzZXWY8NV3QX6O3Yxgvm9/PT3/TV4CKleWqUG2+8mVOrpyiKgpOnjlOseqRyLC7uYm1jlaJSjq+eZMdmDiFfddVVNKnhxBPHqao+L3jBjfzhH2zj5a94OR+45Rauv/461DuKMmM6Lrm2JIgg2kZvngEIe16+8CQKqE2nzSzDOFsgljk3KWEqrVfeuuXk3wtKktQYOdoibTRLLbsoHalaioGiWA42OG2Vl+Fc0Sq6oj135rdYTETJTORxWgPAqB58Rjzu2cozXi5V/BicmBByMgEquzIFniY1NOZBhcIr7z/8ML9w6wdYCyNEHNH7bH1EWkpxDt2SDEcGsZDs5woFSdPkSjdTJGngzx68l5f9yW/zK/d9gnoUuGvlUa7+/bdx0x/8Jn986E5iMB556EG891TdLqFuOH76FJVUxDBkeWkb1153PQ898MAEPDt69CgnnjjOzr178N5z6NAhwmjIofvvp94csrayTsd1gcxziBKxaDCOfJx3X74oJOmU13Lmip2xJDvru3ZItozTmPfRTpUZcLnEEzSHk523NgcqH6+hJsZIQ8hhaAuY5XIWZhkOyMGH6fWN556mTFn3CGHWI0Ay9ig24SE9l/LM7e1Wy2a/L07BVM3hU5M2lTwZIQy56/QJ/u+7/wHzkWu27cEl6KaSTBcXxkW/RC2zNwFaolP+GWvMHO4tksPh2NWveMn2bXS80lNoVFnfiJwYDFnZ2KQQ6MzNc+NN1/GKL/9yLr3sYJvD4enPL3DRvov5+49/jMHmiN07d1JVFd57hvWImBrE4JJLD6K+4KqrruD73/BvefDBQ6wNTmHm2hoeM2Sf8/JFI5p0yiuawVifboEYT9hkOWs8MZtrNQ3VBwKahIRk0qFJhnNFcDi8K3NWtyS8+Il7grbKrC1lMT1vOwbHsEC7GItBbJVOwoj2ZC7ac7HgPasozJRglcNl41wM0QQp4RJEn+g2c3zokVs5sLzIGy69iWNhg36/T6lGB08CYoKkWUtGAWeJpnVqxmQrQyjxJBN8YTQp8i8PXsO/PHgtP/6R9/K2j3+YA3NL3P7db8JRk6JC4bj5hS9g++J2RrWxa9cuADY2Nig6jrvuuou9+/fwuu/8HlzhOHz4MPsv2se2bds4cuQIG5tDFBjVmXp96NAhmhiQ6EipppQKi27CCH0m/JHz8vkvNgndf3bv9cwQu4i0aREz/BXNiYaBvFjOb1vOiZsYTTMgpZyQiARShBBrME+K9YTUPsu/iTG2YdwAaqh6ghliDSK9vADnGHibjLlVPicYSJZpFqHARDs6l4HO2ho6pvz+E/cwSg0/fu0r+M93/jUv7V/AVXN7qMMIei6Djo4cviWbey0lhGT5c0w2C0DK8TRUHMOY0AbiaAPKgtPra/z0R/6Mbf0+33zptSx1K2648QU0IoiHiy+9mIsOXELpHJZynY5IRFrN/cZ//2bisObU+ioHD17CzTfnUNy3v/7bWN6xg5OnV/jBH/4RFhbnMHNt2G5MuHrWz/+8fAHK2aJts0RBG7s4Z8zPiRsiQtM0VL2KSipwEEO/ZewKeEHMiJbLOkQLWEwtAQ6aFHMoOwyBFn+MTQZjUyLVHtrUDizPq6fO/vqnybNSINMkpCzTh6kUYozUU5pw95HHqFPgpz7+btaisGvvDprUoEUGmbI/qRkHmUn6yBFcy3UlyGzXKDmUFiyj1pUIsQP/6rLr+fCRo5wg8bv33Yf6kivnd3Ht9n185APvZXnHLo489jhlp6LT6dDv97nimqv5xEc/xAX79zFYW2duYYEQAo899hgX7ruIGBouOXgFjx0+zL79Bzh+8gTz/TkG/V7LWBSSGhrGD8HYWk7gvHwxy9lzmM6IbkRDvZJStk+3kO5IRMnFw2PMlIXxtgRIGK+piSSCis9lLHyO/HhXQgFm1ZbzZqIanF45wWBtPSdmtpSLM1Mpnit5llEYD9KGaWfUbJMafMt8rFNkx3yXI6c22CwTb7785fTVqBwM8BTWltGJAdOt1dE1aUvm0kxTVsEFQIQSxUwxhSokXn7RPj78rd/P8cEKr9y5j9NWc/3SbppovOhlL8ebY//FF4EU0ESiJkajAdt372H15IkcBos5T6IODXWs6Xf7jEYDHnvkYXbt3sFDhx5iz67drIUBRafi4v4CTdhaE+W88jgvYxmvhZnWkBnUMpNzJSKQApIi3nmCGMRM2VcZp2EkQsqUh9QGKMauy4T1K1vLAuTcLijVMVBFUpjMq7MV3X4u5Jmn85OTrHLoKYHlMKwYdMsuzWCTYYj890Of4pZHH+XauR38yg1fx+/c84/sr+ZZvOQyjAYk8/lNwKXZ49NS1zXzR7AMlLQ099TiDTFA4wNFqmgwlnuL/LOL5nA+s14Lc4ykwBWerizNZOUKOOGyg0uM4ojKFzjJ4bHLL7+cEHNNjkI8119/I4Uzdm3fhbiKJgRUhFEK2QqZVLI6j4GclywikjEObVMCNI+NJIpvyYdJjEI8po7UGFpApM0FSuOKdg6xRJPGQMEUiDUiKkXO62qTIjGXSz8YjGLIkU2LiBQT1+lcyDNeNqMElJby3VLZEzntW5qGRhOfOnaCP3roLpxUqIv84l0f44NHHqXbKfBtJfXpg/BbzP/QotcGuSQhuQBOGywlSkRFUBHEfOZ9tBmdlVO8JZACEyg11zLNmcO07WIy2a2xhspVGaxKhiU3UR6atGUYesx5is48vnR473FV2RKL2ixTB9MCQ+cjMl/qktoQKmNi4Rjfc23FNgNJQtQaSYIpRJsqiKChJYtNmaPjrOt8oHEphhZziWmSna7JJmFdyOM9pcRniQM/K3nGCsThSeTcEsn59oxpGt57fHBUZeB/uuEreN62DjpSDq+f5A3PezHX79lHGhe52QI6TRPWijbdG1qfrp3wY+TFmSeQw2UFigYhuhwFikEQK9AU2zBWTlMXzQ/WzUR3Ml8lK6NZDkdh0wxZEYihfZmWFUjhZHI8YFJm4HOdFXlePj/EiW/d/K2FtlKbjDcO/wtFzpjRzC0RyfVVXEtSm3WPtyQ+2rgGcKscnE4TVTUvrEFsGn0BTM6de/3MMZDJjTAhlJnLk2cUE6bG1csXUDWRP64D81WHn7jiq+iKYjHQQVklUVa97Bc6bQv3ZCUUbcYCaYHsJFCQCExRbkEYaKKMLbkm5fyiplVuqa2rkSYZlGOWYPsCkMl5ZvGX2Gb/jnkAqi1xR3L6lmvzHnTS3S7vF8kK9bwi+dKWTN7SJ4GW0lapExGCRZLFiSJQn4gNtIHayXHGdXufqu6HtmNxXIfXGTQp5YpzAhbaPDU7dykWzyqdn5a3L+3XM3ElUduIQhWPMHTGm65+GVWnx9rKGqU3QgistwWExrU/xqEpyMpjapW0cfQ2vyZMaku0bDuMssnhXde0Kd9xDC7lFHsnQogx04BngK38mc76YrKlM00OFMnlChRBW+xlXHBoUniIXJaAc6jpz8sXhuRSnMB48ktebJybjm2HYCnXx0lFLiWxNQUij8lJSagzxqhJXvqCOQrJhZkcRp1bFoC2IWCEmFKLGz5FHd9/ojzjEZ/NsLFf1U4esQmzLu9U4JKHsoQ4whdtGcExVuLGMfG2OO5Yk86k8kNr/s08u0nhHFolIrl04WxbiFnSToqtBm6BKJ3ZZ/w5W5i3vfhJ8pOqa8k60ibKnSHjampm56Mw5wXIeEYuRZHHpdl4bBckzeMtWMqFBJpESpylYPgY8Zv5z2yUsqUenfnpyBZOjLlE5Hhcu3NoFD+rUT9ltekERHUCUiqWhEYDmNDtKGIdXFtYNlou1SdtfQOv2QLJNTenGa0TJdBiH1jGLmarWhkz2ZJpasppS/dNyXLR4TFrj5ygF/MBsssxVkjj/z9JZiyU9p5VMmU/Ym2Nz6m/2mypvj59a5P6PJKbdEbydYo+uWDQZ+MCuTNbajB2q85UdE8+lspM8SDJPIPxNT6dSAtcn5enl/EkNgHidCwXZZmbn6F4czhXsLm5SaFMxsGTLI2noJqPOSNn+z8Iw2G9Bcz1/tz1j3vmFoi0E08mllpeyRE0GsEblRiRQaah2wDM4xS8ulzvMk6zCKXFQCZEMsvuxvRRztTDHNOD225wyUfEBOcUUaPnPYXWqELfj3CW6Pmcm1CqtFXLhMoHum3MXUpFHZQmOJ1WpBqfSyd5OG2cXnOBISdGbBVW0gAe1OWaEKJG1EiKkeQyB8CgrSUBbeMEojg8QhIhAkmEZOHpn387eMarW35CMsF7tvTCOUvormHGdbNpNfsJj0+ywRvP+G5KYwXcgsdn0SVypgI7m9X2RS5mbe0QsS0LFIBzBaSakGJmlA7W2ZKUqrlWLUxB1enfMnnv49ywpNNujgK5XQYN1NlDaGJ2Z6qqelJrkedKnrECyXTz2GqPrReVQlYiMTZ457C6ABUGKXJ8MMQst2EI0rTNh8bZiOOMQQEyOBllXE42K4cZGg61RbwDr4KTRJBIqQLWsBErYhhwyqqc2oxQtVZN0XbDGwKNgyANPgl1yKaR4KerQGJmArQrhOaqavhEwPAuX1dfHRYihTpEc3lFyMVhCqYrt4jiPCQfqCSBplxNPiU82eedFkOeeeazVooKiey6TQfpNFKlIpPJPdEJM5/O3FOubDl3osWPGLOV2GIVOrKSOFvfnezKzRa2/dJz61RzlrZL+V2NlbiZMd+fA+3kFAqDOiTWTp0gWZMJZGTMLY+7vBiMh+M0uVQn5Skhz/C4MoAAACAASURBVEfJYRwANtbWaCxMSoinlPBlcc7u95lHYYRcRXpiloPE9iF58NEwdVB7PMepY8k7P/0IIo5re+scbSJfd/HunESHYiHmm22rl0nbb0PTONaSY+BTcDVQUjAMkcoVqGvokvGOoSp/e/hx/usdj3PNju1cv6Pimw/0qG0xs/RM8K7BGsem1MwpDJpIvzTqqLlNREj4MfjVhsJs3MzaFNMEMdFxwjAkek5YGwrzHcdaY2ghlAZmAqpIzOE1Z9lCUIQiCSPaVX/sq2pbLdbGRaTjpPmVZaIiro1Atf0+8eO6spIBNIfRSMInbetWtO+M6WeuiC5tf5RMBrRW46SUe9R4XM6qFte2kMjFl3NxY8sr5pnU/dmmWnCG8pgJQ36RS7buXBuh1DENBBFhbm6O0yvHceJpmkRROk6dWqGJgU6vR9XtkSQ3G5u4mK2bPCZtJmvw+FxXFUccW5QI9eYqJ1dO4lXbVqu5Jw9+WvXvuZZnXg/EhGhhy6hMmjViqbkuaOGMvjb4QvHqWFkZ8fJdfe7YgG/etZfNtryakv35bNXkAsVujAXY2DTLGpfW7HZa0hBYqDxNDPw/n36cOiRIQwpgMEr8ySOn+Nm/u4M3v/d+Bs0ipzcHNDHhMRwF4ge86cP38L3vvZc/fegJHt2sKZ2hLSEtYIhXxiXrRHLFeE+EJvJr9zzAex46yeGVTQJG1QEsUvrMchEHlUtUYhQ+0neWM5AdlOTr6DuoCHTNUK0pLT8HZ55cQzyndI8tjFIzR6YrQtJcLFpdxmQKyStfIUrPfLbITGnIHQKz29H64qKECZM4UOHouNaNc0ZPFK81HVWCNBStKT1+9yJK1Nz/ZkzqcwqYy8C6+RnlIdNB8iUimeCYeSDShvkn7rkKc50FxLR1w43SOzZW1jhx7BirJ08Qh4E61RSSo4Edn5+vE6UQ6GmJ89mNL1QpneLUs7p2ipNHTuRiRSmhktuseCctK/XcvINnxQMZp/GHNpArknuAJHOoeAZxg8cGnjtPbzBcOcZjqeHQqSOcWC9IC0rYzCtfYsqlAHJhH3VAk3NgzEiWV8SQFSpqhgpsRqNik08c3eQH3/c+fvOrX8C/uAR80UVcTUhL/Pwr9tMtN3nxO2/lwEKHX3rplVy21MWL48GNwPseXOUdd30acXO8/WX7+Z5rLyP5nOxkIdfCdFEnNGCvkRMJ/v1fPwbxfiiMV+y8gFteez1JEkX0NBqzElFPIGSXIYVctS0ppoEmDdC6x8gHOqWiwaM+UOEZhHEF9RyK82P3JRjDMlJGh9eaENqG39ExKpS+BuK4PmvIdptKxkncjAWQ20Hk5gWqBQMivs0cHUqiYyAaSKnK9Whba0qZhhqdAiR8cqiTrKhcwsRDmHVrv3QUx1hsnEWrRrJcUSxFEJ+f49ziAqfXj9P1HUYx4RWij3jLpSbW1taIMebeMS4XAE8pTXtKixAllwOvJSJ1Ym1jg5TAa9vwWz2qBake0N02l4me56iJ+bPLxrUE4lDyp5nhRGmiIGmDueT4tbvv5oq5Pmsjx9WLS9ywaxdPDB5iNGrourG/3eTv50yi7KYAIo4o4/qTzCQltXky1tLUxfP6Sy/kV25/gO9836385PX72L+8RCdU1H5Ityz45PFV7jht3LdxmG9494iPfssNLBXKysYAJAB93nDdBXzlpRdCEWhCzo0RMuFnHI1PUUA9c5rQKmGNx6LwMy/bBzpAU8VPf/JuHjwRuXK5y427F7msX7JzrkvTCIVP+MLwUZFQ8X/d+wA/9vFP840X7OV7nr+bl+7ehbMas1xkN0/z3LsFidQeChnRxIqYIj0XGEVw3vH4xgZ/ePdhrtnR45UX7MDpjAvG1hB5xmPa41rAS6CRgpXNGiEiRRcrPBYjvj2Ok8x2zC6rYBFAQVM+TxAaNUpLTxHN+tKRcTg1tfwl2qBDpgZA9CU7d+5m9eQmYpuYFbhUTkgf4/YMKYJYQ12DEXODK9M2MDHtuKg4CucQv7X3jjgoKk+/vzjpg3QuyGTPnEhmTPtrkAPQYxfEfMPgNPzoh/+BS3t7ORJX2Te3wFJZcNfGEfZ3y9zHI20CnbyUmaEzFc1Fsp/uUh6UXhRTQ9IU0S5VGBjMxYJrd/VQHMPG8/v3nuB/edES675miQ5EePsd63RYo0x93v0NV7BUVASruX89Ia5BIrz5hsu4sJ8B3OBqfBKC1pPsyNSAlDmvJiCkaDnBT2ouX+iAdViXyMOnG95xz3HgFF+25yKWi0QTe5yMpxk1ygjwAr4suO/4OmEz8Ltrj/K79x/m6Pe+nEXfAdr6qhPcg7ZqFUhyfNNf/x1vuvZKvubiPoSAJmOxKPj1Tx7lBXsqXnZgBz4YheUCTYUxCdUCbSQlV/42TcRQolrzEx+7i9+5/QQH5o2rd2/j7a+4hvlOhQYjuZmOG23Y2Pu2RWYANDdXiJoy/ZFZN+ZLT6ZxkWn0cJIV66HTX6bozvHEY4/mCKbleeQkt8NsLGVX04TcNSbn0VgboRtH4swMUyOmDJiKSFb2Ar7ssLRtJ94r1gYPzoU8KxB1LONk4nEj7G3eUc95rt++jVufOMq1rsMnjp6gUKNiyE179jKMI6rOYmZytlWDMuY8Bt/GTbOzEsFC7ufQSsSog5F0QFRjkOCrdm3jIyfW+Z7nb6MqA4u+w+n1VdTBOz/9OP2qx+++6nnccnidnf05Voej3HZQu3zlgTkOLJBZpMlY0pLN2DBXRmLMpfV9BXWIiDcWSsW5hKuMeqBURQUCc+boFY7KdRjV87zhugv41qsuomfCQBISjMI5HA1I4v/45Al+6MN/D0H4gesOsLNTggiDUcS1rR67hSfGROUcFgYcWtngE0drXn3/33PVdsevvvxKXrl/J0te+YYr+1y5OE/VNJgrEYlUKB4IIdeXjW3+0Li1qJrSLYWQKuY7DitGPF53udIJy2W2DKvSU2OUEnGTAEuA5EED+C4PrK3wXe+5mx+7+RJuvnAH8wjO5UhRE3IN3TP79X4xi0kihtQSKAXVcd8WoUAZMmLOw4W793Bi5XFkZLgyL8SNBXqlI8WIxkhU34LkrTJqG1pJVIIEfFMhLiGqjIZreFdRdTssLS0RMJpRItajDOqfA3kWLszWmoxZMtAWkmMjblJ5R3+uy1fsW6QewbHTA37r/hUWqxM42cZ1yz0GEpg2k0oTenwUQ2JbUVIg4ihcIkVFJJEkEaWgIwX/+22f4vGNHlfurTiwuMjp9cDvHTnOoAks9hb57bsfR1zirTdfwa/d+Wlueazm4bURe/sdUko0OuJ1z7ua9SR4y+0TP3T4GP/ub+7jx2+8kNdesgtVqHwxhg0wMTrJSMlTOeHIYMAv/+Mx3vj8PfQ9jIhQBJL3SEwtEg6NK4gk+pJ7CncKWsdYedX+pYy8EjOpKAlIxh9ASWGEE89b/+EhBrGh6AoPrI/41dtO8sr9e2jwfPOBvXzvh+7jmy5bpGdlbiBNQW0Z4I7Qrk4Rb44k2eXAPE4SpRQQKuga73rFdRTe52REMx5Z2+CJ9SEv2LWAAmXRZTOs895HVvn1u+7gg0+s0SuM773lEK+++DF+5qZr2TvnQSPqPCE+Fev3i0+MCFrgfIMlwblxFC13FYBA15U0FvClY8f2g5wsT/PE+gr1ILCn6zi9GegWSq/0YAXDlKhcdvNplX8uYl60BM284PQWluh151jctsy4JaxXclT0HFHZn11fmDZspG3DUG2DraAMm5KuGBc64ROPRS6ZE+a1wzKJW544ydcdvIyhrNLx8y3Yk/UyUiPSw5tStxgEWFtOoWV84uirI6UGKPi+513OnPesDSC4yIE/+ChhaDQxUVPzkosWeOO1BzgRNviaC3fzkp2GKzb5b58+gtNIxPPpkxv8xsomvQoaLfjRv/pHohR8z3se4G27P807v/afsV6P+LY/vwO30FANlA31EBsIxmve/UkePr3Gr91xiFfu6YMFCom5Z6/VkLo4gaYOucu7uLxyh7ptshrza7BAMo8jYprzGIYp0NWCGAo+cWqN37v/OK4QmibwH64/wH9+6QFIQmHwkr0dTtaBH37PIX7v1TfQmGdVAwvmGLRd4BHB2hqcRZvwpZJoTOm7DhBwJrhCsRRIqgyCcffxyGv+31u4bPcFfN9NF/Loykl+5/Z1VuMqJJizitOlgl/lt+8s+MsHPsB93/41dEtjWEdc4RiFkAl4z9XI/RzLUyW4OS1oLOCkIBBxreXVKTwRGNbkur7DwKY0fMd/v5V7VoXrds8zGjXcs77Jd1y1h5+4/gI2a8ccgSIaFqEJgVJSm0qSK/k5V1B1+/SqDnXbciVaLioUUyKJtWD654kFktqIQsaHsr+biBnwjENO1KssLZcMqDi5/jhXLF/K229/gJ/+ioNsDiIpruGlR7SQu5Oj+XctW3wloSlbImYZSEwUSEtFD6mh9GWuHGbGKCYWSsd//MSDrA1HODxVRwl1wacer3n+/CZfv/ciQmWUBKIZP/nhDzBwyo9cs4saoRkW/MWhR3nPwyfBKQtFwYv2CIfXhY8e3+C1++f55OtupmpbGM791/cQ1JOS8Ltfcz3bnGNEzW/feRzsDprY4ZZHn0AlcWF/kxJYkJL5Oc925+i4XDgaiVBHGmmBJGPSsCikRLdMNNbQLTw/+eHb8K5DsoZlr/zQTQcYJY+aUbiENj2uWfL8yYMn+NU77ueHn38QF4UYh1B0qcj4xyhlLkrQiLQZjSLQIYEqMeUwbWmwXtdsqwqKzgZ0u9y/scqPv38ApUDcpO9KLu13uXxPhxt27OSr9/XZ1694bA2SHzGkQ+HyvXS8p7FxotkXrowVx4R1a7kWx3hblEgpnlGIdAttXZj83Uikcgqm+I7yur94mG+54nJef+12OqIkVY6uJn7uH+7mZ//xKD/1zy7E0hzL6hgGo/RjLCwfL6llJnPILT1VXM7GFc05OCpg0tbjOjfw9jOvSCYeo8lWA76lsjvElKEXlljkLz91Nz9440F+7O7E5uAIX7dnOzH26HlHKA2XBhRunqRThuW4Y7rHaMRwUQiS0+rHHVBFEl5L6jCidLlOpDYNd66u8UsfO8FCJ/uCa4OGVKyzkvp8zbse5IKdd/G9+y/kzTdfwkPrgfXhKhfM7+AXX3pN6zoktt+ZeM/jJ8Enrtlh/OU3vpxgBkkYqKdIIff6cEodSwgBUuTKfoelroL1GPE4WAXAKCr3rQx46NQmqzpgbV04NdxgMxasrQ+5N6TcrLeeZ30QsJbentMZElXhyeiD8NEjx7nloSFuaR3Z3M5/etnF7K4qBhro4WjMECd85a7tfOSRFd70D4d55d7dXLujwOhTamKIUCFUKAMb0rEKTWugfbxAt1uCDun5JTxCY8pPffwBfv7mC5n3XYhK1fO8+dqLeN2Vy8xLn/5cB++hMIdJDanELLBcWnZNW8BWLGeFfjHAqpN+K5o5QqqQU0kzWUvJTNR2b3yb8qFmrA4aFjq5z9E77jzFNYs1333dMpIcG0R0aEjZ8ONftoef/Juj3H58k+uWOgyBygnJIgRHowGPJ9WJoC1PJ2U3xqPEFHL9VGJOcJWAxM8TCwQzTPwEY87AfC503MfTzI24dt8Ofv2Ow3z38/ZQaeDqnQsMN+BofYpO00M7hk9gsSG1/lyu6RGJLb5iLc3VxKbemykaA4XmsoKVwPEm8up33UnVG3LNzmW+5cAu/sPf3cNOWeBbr9zOJd0uv/XASX7xjlMMCRwbOKxY5geef0EO47bt6x8/GYACgnHFtj1AQrQh0qejgTo6TBrqoPTKgs06ghc2G1jq5dVoviiAElzDvziwm399xTJlyj7qj37obq7fto1d8xXP29bl3tMNP/rR++gsJ/b3S6xln6oWRDEGKbLgPZu18Z3/43a0Atno8tWXdPnh6y5mMyjd6IkIpcu5N//6yt380q0PU4fAa959G7/8soP8q0s6rDaJrlYkN8pcEYSPHD3G+x/boO+PUYnj/Y+ugXo2LfA/7j/CXz16lN+66yh3HDvN9123FyRgUrDs+xyYX2QzOQoJFFQ0qWmJf5n7EDXSawl/1hLzinG04YtGZu7FdMqREWFzNOLQXfcwv22BwgrmF+coqi79wrE5HBJ9xa/eejt/+U0vwsUOSWq66rl/Y5Nv/6tP8BuvuoGfevF+vumv7+Rvvu4mSi0YWKJDzrFx1jKDnWGppby3zd2nZTYBMsalbM1qfy7l2SXTtTUwxshHJpYpqVaqQnjx3mVesmeeVx3Yzou372IhKUMX+P27jmEaccmjZHIMkuuBTJlyqf2xTDVoadVCjvoOiZAk9wzVyI998FEe2lzhtRft5INffy37F/psK6F2iTJ1+cUvv4oLijmGzQk+ddx45/1HQGsuWOjkSIIABB4cbUKqUd9lf7/I4CIVIdV4PM7lsFlHYThSfFFANLodn6/VjCY1QA1EtlfCWM06Z/zBQ0f4uY8f5c1/ezd//vBxvuHgNh7+9hdwx795OS/ftw3RQCm5oXOKUPhM2vvN2x7h0PoGSoF14f980cE20xIGGFXRZkMDly10eeNNFxLXhxwfCf/mvXfwte/+GKcHYLaBjKoJx+DaHQt8/zU7eO3BXTy0MuJPH3kUmgItHUcTXLu8m//4oqt48Z5thJGCCD1fMt+F0glLJXRdrk+nYjixTNlPkdIpQxoayaQ8L5rdly9yERE2B0Puue12XvaSl/Jd3/F6vvEbvp4br38+e5aWeMObfwAT5T1PrPGKPXv54BNr/NG9J3nw9BqhbnjnQ0d5ZK3hDe+/g11d5aZejw89sYK2xZFDSDOZuIJOrAprR0D7l0qbxqQg9vlVkQyRifKdWiB5oGxKQ68p+dSRE3zbZZdyOqzRKZSHN4Y8fHxIv4RF52iKljcwtj5af7Llik2iPBqN5ALStIWLQuaFDBz0LPFbn3yC9z9+kne9+ma+8bJ5/uy+0xxaXaegyxVzcOmSR6Pjba/Yw1s+Yrxwl/AXDxlvffE+/uah07zuyr04HFji0fUaVEnNKpfMXwKi1NbQ04pogZCgowYEyl7AhYLgc2ZlokR0iAG+6KPhFIUqAUcpAIlKKk7ObfKa3Uv8zAsvYxTgjpV11prIi3fPZ0+KhDYRddCJBbVFfuPTj9HtbaMZrvBbr7iB/UsLqCQ62tBVg+TwYmCZIv8TNx3kPQ+c4u6NmsoVfOwI3PgnH+Q3XngN33JlwbEaSjEW1VP0PWLCQJuWyGQUMfEdl++kUsEokBR5/9ETKH3MGupRwASCebzUCEo1NtN1hPmSCFTWoHgG3qBOlC53a7OzpfF+gcnZ0u7H/+t1O1S9imuueR7v+8D7KKUEgbf9/Fu59+FDVEXJ4/UG1+zdTjet8DO3HeXIqRN88rtewRuvv5Qfufoi/nF1lQ8+ssFrLp/jPY8f58v37aBPbiqPE8xyJK223BKivYrp9VkG5nNy43PfD3dWnnkUhml9C2Pa1CFXV1eeaAZ8/SU7qX3CNmtuPbbJfCnsmDP6HY+6Ct9sTGp1TCyPNheENgls3Ig44XO0M+SktIaIBuH2lVXuWhvxvtc+n/1zc6ytj/i3H72T0wNjvuP5yr0XcPnyAoMGXrV3Dze9Zjtv/fu7ecdXXc4/v2SRV/3ZbRwbBPZ0S+oAD65vTkKpNy73QDYpW/p2EohWA54QBR0OGNoiSOL37j3MY2ubrA+Fw3VDsA2QikX1bI5qyirfTNQRbrUkjDwWGx4aJb71L+/m6GbgLS88wPddu4dKS1xR0IQRh63mh973SY5tJA4uKr/wtTfx5bt25HqXSQnO6Jij0RxZkZY49u5HTxJUKYpIE4xGamgK3vDRQ/yX2x7m5ou6/LtrD3BBz+GDQ3zg9uO5G7wjYRZ5YnPIvrk+a2lER0q6VoEMCGmOoQohRAJG8MIoJCrvIDa86f338chwhbe86Hkc7Bc5VKwdRHP4W9LU1P9Clq15JTN1PCSHEyQJhw7dzw/+0BuRJiJOue0Tn+C6m24mWqIarbP22GEuOrBEubrCH77ycj567yE++OiA+4Nx8bZl1lPkmy/fw+bmGk0bqAuScCa5mXa72iqay0oYW+YStPT1NFP/5RzIs4jCpCl636qPSWqxGP1uwUNHIu+/83Fu3JO4/8Qal/aUQxsbvHBhgduOPcqX7bpgwspP6nJ6s0jOfAWSZRJ7G6NBYnY1TIUuCv8fd28eb9lR1nt/n6paa+29z9zzPKW705lngRCSmAAmhAgJg4AooiIIAg7Xy1V8mRQREVDvy72oTMoocJlBBiMQSEIgCWQOGbrT3em5+/QZ995rrap67h+19ukOr+/7eck1IKl/Tp/uPmevvXatqqee3xQN25aO89ZFLdo2x8fIu3ftYq7v8AKvPm0rf3TWEmZLeOInbuKS1Y43XLCFq1et4sz1owTvGCva/I+7dvOGM7dy60zFnpkeEnMmOsLmxePN4hjoRrh/ap63fGcPB7Xk3slJunkG/RKw/NkPtvO6Mzfzyicu4kVf2wHWgWT85nfuohUjFRYXDLO9jCqb5+YjgXM/fxdzk5PsqJTolVdfu5v+fJff/7mteA101fK8z9/Ed/bPc9byDptbBW+5eR9vD/cx2h5jUZEjAqNFYMTmrGg7bEvR2vHHN95DMJa6bpNns1ReiBKofcX1swXj+wuyM9KZ2LhI0Iw7Dk8Ry4yQC+0QuPzjd3LCymH++gkrWTU0TjCRGAqi9piNfe6cKYl0WeIyhm0b4jxf3jXF39+5B6Xii/dfz78+6wzOX70MiQkynvcBY+xjppF6bDy8F2IBm1s2rt/Au9/1LsQL4oR3vO2veHDXDo5OHeCsXHjNoV1cvXkFqyeEZ3zxNk4dGuF3HreM31+2lCg5b3pgmvsmp9g0PsKQUyoMuSaW6syRI+Stgs7wODWBrLFg+NHxaC0ax49HwAMxoAHENj2QY1NiuHDEsuZrO/ssGYIHZnrMBmUmGu6dqmllcP6yceZtSRvHIG7PaVJ2Do4zmtbxBaPlaCJZMIQQ6ZFMhFqVcPWX7uNIjHRc5N59gRmZwdHhpj27eK0PHJo7wp0zfe6cLfnHHx7kny47nXaIeFuzcbTF335/N7932nq+sGMP6gyI5/IVK1ECZXRkouQq5GL55507KEx6cI22MFkPX8PnrjiVJyyZgAh9WzGmSmVrrn/a48nbYGMB2uPEj9/Mgemcs5cs4TNXbuHBGc9c7HO0p2QCWxfluAxiP+MzP7yfa/fPsmWJ4WO/cDYTQ5FWbZisLZd/+RY+s3Mn1JE/f8JprBwZogoVh+dLZvwsJw0P852ZGTIj+NiiJZY432em3eapizI+ctUZaK20nAU89x6eY14CGnOELnMm49Xnr+Bl37qX6z++jx/+6gVkTsD2CL7NjXtmOXKk5KD39HzFl/bOgFpyzdk00mbKC1Ol8Fc37eEzKxcnIlQIZI2ymZ/9E8yCy90xf91jR4RuHZiePMrdd9/NZZc/jRXjiyhGhrjlpu+x7aTTmJnusnnZYg4fhKN+nmt39PjcU7ayZc0IoTTMBXjnLbdy3gmbef89c7zjwhMoA0SbHPz8XI+57iwyO4tkOR3XQhGMkUfNNOj/azwCKntIij+O64E0k6IykWhqgszxwyORC5cO44oeNuvRrTwnLsmofc5oPNY8HZRetlkwBm7pXlLgVAooTibKIkKLPJn5OMcnn34iMWR888A+rth7G7bOuGD1EH/8uK1MVl3unx0h3nUI6sBUK/JfvrWby1+4iBaOrWMFs77Hn39/O196cIrMOqTu88yTFpFLerhEDaUJrBp1SYBXC9YpkZKIg7rL5qExkBps5GivYjrUtNwIVaZkPk9Ijx1Co2XWeYyZQmLGhnEl+DH8hKC1YDLHvJ/nQD3Pa245wCu3LuFNF22hyHOkBOdgZRGR4KEGrOXS1cOcu2oCA1SVUjr443o3193Wxdg+//cTT+Ws5R1e+o07uOXwPO+4+AIsgVoMFYKJhq8cOEIRhTrrYqSN930uXbOaTvghk5Xhfbcf4vzV7ZS1aiIvOWUtV29ajCcphE/7yHe5e36GLaOWW1/w8/Rjl1ocEh0hVmiM5MZSa8Qa81OZ5P/R43hLSNVj3qNilI61LF68lBNPOpl3vv3tTE5OMjk5Sag9zjnGOx1GXJsXnrmcr26f5J0/fwLP+8ZOfmvLSs5bXvCJhw7x9d1dnrTJ0hou2Tha0LLCZOXJTcWBQwdx1qBRmZ06yNiy9SwkrP4UxiPwA7GoDnDuxoqtsdLr9B3zXaHX95y9ZBiP57DOEcIQGZFP3r2X4SJLjlqDpLhGTDcIxEkJ5KGxclNM4mYufGhRDdYJM1Wg7Sy1KH92436oc0LI+KNzNnPW0hZPWbWajmYYySBrsaUzyueu2NrkbkSuWLWMGAvefcd+9lQ9WmI4Z/UEl61fTm0CQo4xypBarDiuXL+G0xeN8uWnnclIntGxgCvwmoK60Zx9vUhejLLFWnYdnWeOSKLSls0iqWSMoqbkm7vn+freI/TKNPFEA0Muh8rwxcu28acXn4GxGXgDWXKYBzg07yGzEC3LhofTZxAUtQ4bHP1ewLgebWlzxspRzhob5dqrnsA1V5zF5kU5JuZYF2gpRK342N37sFmHF25Zi9cuw5KzqmVY0upAO7J9bpbpSiFkhNrQscldJARLzyZFNMHgJU9KUTJ8CGQCooZMhF5MUzzGn/3+B40j27FH9ke/hw984H3s27ePT33u09x4w3c5fPgwU1NTbN++nR0P7uVIb44Xn7SBW/s1h+phPv/sn6O9POebh2c4Z9kEb37cUj5+67288/xtTLQKjvqaRUXG7EyJjQbxkcw6+vM1R2eOLjj2/TRkAo9MTNc0aRqqRnrwRelKYGluKYo2Nx+dYmO7Tce26JeBXz9rNZ+4c4oy6+GqjOgaXUSDwMggcl9nKgAAIABJREFUMEcaaoZXanPsAk1Mju6g1DEwXuRErfjuvklumDxKlrU5Z5ny1LWLgD5IzYd3HEpU8W7JH/z8aZy4qAPUKMIZK0ZZM5pzcCZgtcZJwd88YXPSvmDoa02OIqLUseQ3T13P0fn9XLp6DGcLfDUHIeBME20php09j+31mFjf5pyP3c4pyxwvPXk9V64bJ2YROxvp97s463jNjfdw88FJNneW8rYL1vH0DYvBODaMjbBW5rFW6EVD2wr9mFiHM2XgcKzARyZabZYPJU96jCGoQbLAVN1Ho+AzWJ8LuIy2dLl47VIIkWA8ZQQVz00Hu9xztM8zNy3mtPEhPnifRQtDxyhbJnJ27ZrlkmWL2TEboajBFZy0eCmikNtkmJNZQaziiohIpG2Fti0ajxBLXZFYqD78VCb4f/xISJIxoDHZbcYoiDQm2xZe9oqX85xnPY9oA9VsxfTsFM+46pk89NBDlNU8uVGO9Es++MwLefO1u3nW17ezcVGH4TLwpfvup6cd3nfJCawvApUqwzZjPtaMjoyjRpg5epgiegye6elpxsYmiCahlD/p8YhQGB3AJRwTwcUGBUjBUSUbWovojBzlC7dO8xdnb+GgnQVbY3wLL30K0wIMTpL9HgOHco0YTfi1jZLcuYJ7mImvE8NM5WkVwuF5z7ahEe6fnmfnnOHnP/t9Ngx1WL8o8t2DR2mrsGj5EL98wpKkABaQoNRxnsctH+dz89NEFzihnXPe0mHqRPRmyCQ3jprIkOlw5jJl1K6jHyzlTE0vi1AkqT9tuP3gNKGq6buKK9eeynf23cX2ruefdxzl+p2H2SSORUsNe6Plwo/fzJ1zJdic+2f38ae3Zjxz0xKiWioDqkNUlWCdMBd86hFp4Gu79iAmIy8MZyzqpI8vWjA9Wgp9D4f7fbS2BBNQYwlRiDLUEEOFlla0sPSN8KE79zFd1zx1zSj7+4JIheAIBP7+4jO48+gUF61eyptu2AGhjTGe1Xnq/gfviUbp+zpBZrUjWe455kMgN02CoQhVjDjz7zf6fhbHj6Iaxy+MCqxetZax0aWU1Sw3XPsdfvGqZzAzNcPRqSNkWUbZq9iwag1eDf/XhZt45QWbuOPgNPNkrGGa8VCiOfTjEKNGqEPAqCMSGR8eZ+boYaLaxAAPkaiS8p1/CmS9H78C0YFLCgxiHUxTxg3ZjK/s3c/9e+Y4+cQWB6dgy/gQO+emCFY4d/kEtu4RioJYpxS7MJDvP+yNx0apGlEcxjycPxBUGHKGECPPP2ktl29bzoFpzw379vKZ7Uf4yIMPke0SAgGbZSzLRvj4fQ/xqyduwjkDtsvXd3f5X/ftxVlLHnN29+Z4wdd+yMtPW8npS8ZoIUTnceoIJlnDIUIOlHmNqTNiXeNyi8bItw7OUYnn/OXLOWVcsDloyHj5tkVctXUVs6Wn43Ji9BQOfusbd/P+ex5Csw5/es4KlBY+1tSARqGwlhJoAz0TyKPwrjuOUMRIVwIvOXkdxkTm8Qw3i7jXyP7Z+WQ6IZGOSyQ3o0KlkRgjhcsR9UzOVnxo7z4uXz3OL25cyz/cvjsdt4xgcGwYEzaNLaaulesPTIGNrG4n6jpEVCxBHBoDSjLFptH0mJDU08FqI/FRwsBP5DEwEkG/CZfnmBZm4LL3zne8gx/ccgv33HMXY2MTfP0b36Bfdgkh8Lzn/hInnXYqPR/oZJGZOc9sb5rJG6/jKVdcwdThyJHZaYashbaHMJB6NExXJ7RaHWLfEzGIKnVdk+fZT2WBfkRHmEFY748SVGqZZ3NrnEvX5lg1eO0y1mozMTLKSAbrRyvQnE6oEOeIIXU5ZMABWXiNLGU+NFAqmOQ23twgZ8ArWKvUMTIhhtGJyLaxEzhnxTKu+dQtFAJ9beHEcPuBGX5jz1Ee7Cl/fM46Yp3x366/C6GFxIpYlXQLw6d2HuSj9zzEey4+lRedshQlT7GaqmTNWU3FpDCr3EMw1DEgxvGZHfsYwvG6M9eRZxm9vqdTGGx0uGgZL1IWbztLxsjWG6LLoBvYMD6OhECeJ+8O4wxeIQcCjnZd8ald+7lh71GkI5zUaXHZluUURIQCpMZGixjD/p4BrRnO2gznksyIYuOSBcRY0QuW/37HdlaJ4zNPPx1rDZqDlkpttTlOBrwY5qJw8+H9IDlnTYwlUZYajKTW9iCdMA60DYMIjsYn1ZjkbyvmsSPnX9AUN5Xysb9PVet//YPf5/Of/xde9tu/ycc++klWrFjBocMHmJ6eZmhoiH63y/DoCH/37n/g/nvvZffu3dx95x0cPHqQ0fY4dehzysnnMKzVAIZsquekK3LGUpkS0yQyhkadbn6kAvlJ3OsfH5aP4TjrQWlY3EmvEsqMoRFDpWPM+JLTJ5Zx2ZqVjLRrbOij0WJDoCqyhRcXUVRTfsqAWCaNR0girJmF5DpoDGpjSJEQKuRG8Wro1h3unZrkmV+6mbqumQnK5pGK2fk5ik7JUMdx/eFJemq46kvf5bbZCvUVf3z6ev7xitPpdQNV7JObIV5y/a289Gvb2TPbJVLT80JXSiAZSue0cUKz2wp1DdfvnGNpJ+cpG4aI6siDwQQh2gDGIxrJTaLpiwqlqROVPvdYsdR5en9WoIpQkNTDUT11bviT63YgVJSl5S/O20wHpZJ8QWGMlpR1xVTZB7G0koNsus+mcTfDoHgcgQ/eu58PXXpKCjsKYGpZYC0mmb/Ba2SyP0flh0ANp44NU4VGvmBqTEheuKkx1gQ8o6jYtCdLYNBcVOWYufDP8AiS8lPVHCOPDYYqaAjs3LufX/3lF7BhwyZ+73dewflPOp/Ln/ILvOY1r8HlOUuWLKHIWmzatJmxsRH+5Quf5/nPfz733fUAX7vmX/nQP32YBx68L/nCRIhRUU2AgwI2Sz6pmBR7AiBm4NzLcdfz6N/vHx+FGZxtSSpE0rzDAF0ndPs13zu4m1XZKCcuHWLFsBJji1lTUEWlNjWmbsxRLGiMiAXxaYV1JpkFL4QeqWI03Zxou/ha8c4SVUAC0QtRPF/Y9SBP+OQt7O1Fqkp50dYlfPZpj+Pjv/hzxFqZn+9x9nCH3/rSD/nqg5N0Qsa7nnEKb3jCVp6+ZgnPXzeB1paYdSlkiA/s28nmj3yD3/2XO+llHi0zghf6viTGSVSFIiuY9SXfOzzPfDXHlavHCDKEk4roPF4sXgHNYIH1AiAYKYAAPiAu4AbzUBxGIkFSEFZB5I/+7Rbun+1RuoyLF+c8deNSWgJOe6CKxYC1HOxW2NxCrFiUJ76OCFRBG3NmZZ42WMOXr/45zlkx2oQ+Gya1TDEUMsjpUVoItx/qpg84eM5dMQQ2T9cZJXltYkDMgqOcxORkthBODs2/PTZo7BkOjGIGYWgLOTiNc7+1vO5PXsv07Aw/f/EFPPeXX8jb3vIX/PYrXk70gXZR4GMkyxyXPPki9uzazVBnhAMHDoERVqxYwVXPeiaPP/c8jDGIBecE5wYWGqAxeclESc+PldTQ/UksGD86HkEPJByLOByIV5rRjhWlg5effQonjXWoqj4H+p6bdu3mSavHCcYSvKAFZAABgpUk+W4mbq1xgQeCmMSxMA6JINrBZX3KkP6fBuXL2/fzxpt2cM+s59RlIzxt9QgvO3k1E8NtYoDLVzl2vOh8PrP9CJ/cfpBbJ7u88NT1vPrMlZwxMUEwgSJG/vappzN8/XY+eP8eJM9YBvzR49fyoq0bOTQ9w/vvPsCN+45y12yfbhhjJFdmy0kWddr87vX3sHJimN89ZxMxKhWpesqlZm83cMeRKQqb03aGTmYYcxBDDVKCHyJUIMPpHvaDJzdCiWfUtHj37bv423tnwMKaLOON529lpu7zl99/kFwzVnSGWNIRWlb5m9v3EdSA5owNGVBLTU20Fm2cuXNTIsGytp0ISD45rWAkRVspAR8UZw2o8K97pkErzl47xEVrl+Iau2eVmPgHrmk1azs1VymxmjHQUOtAaQ0YcyyM/Gd11BqTZH5AcowDdClZMXgCv/SsZ7Nv90O4rEAJPLR3D/v378dai1dPS5W6rnjfe97L+97/QV75yldyyimnYK1w552386lPfofLL3tGOh4nR4lkcWjS60oUjBNCnXqFA25NIyz/iY5HYGmYJMMG4Dg0RrD0DSx2BUw4Yj3HzYeOsKIY4Wu7D3EY5YyxYUpfc3oxjDb2hIYm+lESTT4zhjLGBeQBBjkxBqUGn2PyPruPOJ711RuZyDNevGUpT96wiq1jbXJrKMMcBS1qKqzN+cqOw/zh9Tv4tc1r+OglZzLUUgpToHXV0PIjS4qcd19yIi88cSkvuOYO/umibTxp7SIUx7rWGH94bkFhtnLSR79Biy5lTzhz1WJ2Tc9z06FJvnrl2SwbbpEr9MqaWLeYCYFrd07z/clJuj2YriO5UaYM3HPgKNQKUlKG1GSMoWbItJjTkiFt8ZGdu/id7zwAfc/j14zyiSefzvLRAo2ePzlzG++7/wC/d90P8LFNrHPUzkLfQke5cvlSMAFHgQYlaiqFW5pTGk87OkSSB0smUEWbeteND2wthkx7fHHXPjCBj1x4NlmmmDKHosRoirUKTcZPCHUjoM4IEsg0wzR8oWNB5j/7UK5DUlM9OqLEdPyUuuEXgVPHxU+5iDe/+S18+WtfpbBtjk4d4siBo2zddiI2GqJE+rMzhNpz4QVPpCzn2bFzO/1exV233wMObGMDmbJ2IsZavPd0rGO+7GKiRY3HxpDMuCRZZ/6ka5BHQGUXFjzoMQwMhZSAeEMFeC2Z94a//t4O/vqix3PliRO85/uH+Ko9wKvP2EyllmzA4KsHN8o0+TKJexGbXdwgSEj5Gip5Yz6bsWJY+ddnnsqy9jgVJDm9b6Is8xHwJV0X8d2abx48zLVXn8fmRUNkITlxdUtDcI4sWDInhNoSM+G85WN8+qlncPriDlGzRGrTQEdyZrXPJSuW84HZnTxj3SrefskW3n/LPv7uktPZPNYhKtRRmSYH7YEXnnvqBM9ev3LBFTuSYNeXf2U77/3hPvDCvO9RiiU3OWWogJo3XreDN99yP0PtIV5/4QZedebGBPP6gMuVNsqLTlrJiYs6/MKHfgDFJJgCOspiF7jqlLXUKCGkeMW6aWD2axBjUtC5JBF4P3qIFfiaGIValL5XPnjPQfb0lXdceAqbFg9jY2Q2rxlBUbVJ2BiUpPwUQJKk37gGZQuoZog0aXaPgTFI/ItEQtNZssfFeabCLWd0vM2b/+KtnLrtdG65+XvcctPNfOHLXyIIxLKmFMMLf+1FfP6zX+DQoaNkRU5RtBkaG6LfnaevfUZji2jAYfAxkmeWmbIihoCoJl5PFIy16E9h8YBHCOOmk8sgdtJAHDBTI56MTCLX7pjk4g1LWVSUbB1dSVEc5qIVizlzoqDrkoeERsFIyrnAaErQklRWm6bLohjEKcGnLn4/JkSmledkKKoGMwincqDqKaNBbYHVHllm+cvHn0ofkzBzI8Q6YDIhasQ4Q4CkLvWejhFOWj6Ep42JJao5RlJ3vWU9b3/SVv7qog1kkiEWXnLGatZ0WknWrynX4/QJ4e0XnMzGsYxLV6+kNJEspkS4wioWx5krO/y6WcrG0RZbF01QBIuxkZle4FXX3UevX/GGJ23hOSes4qRFo5TNWRcRvLYa5axy6dIhrjxtgusOwcai4GkbFvPLJ65ieRGhtuQWqihkRqjjMcZkpZG2OKJCxxnqfoTxgjw6qC0H52Z44/ce4r+csZKXbFtLGT0dm2Gix2uWgsiDUjc7ZDSNLwoRH5MzlsaMqKE5uvzsN1BhIL1I2hMTHcYKISR+kUZDLRHJMi59yhUsXbmKso7c+L3vcHD/IV7xilckRrERWkXO3NwMKpGrr34mJ2zZxIMP7qLq19x77z0Md8Yg0FSOUJUlc2WP3swckYCzDicOIcPYho/5KKpu/9/G/1Gw1ML3zZ8L02LaV1R9w7cOHOTNZ2/mvqkeB2amWWJyrjxhMdaNUNgu0sRY0ih7tVk8RAQbG6gTQ6RGYspozcSipm7UMRETC4JTxIOxEaKl0mQmW9iAizkuM5RqaEmk723KlzUBE8BmyRU9RMVryuKtomAlx5lIrZas6csEoKOGoUKpYhsrQul7rBsuqH3amSQ4vImcMraU088Yw6viBCI5wUaM2gQHI7zitI340yu0ysnztBgElGVDlo89+WQCLUKoaGcONNASISJIlCa1zzASPP0s59NPPhVrGo9VUWZ8RtsFbDR4AxqOiehFUnhc2zrKEGi5SI9IJpbNec65y4bJMuH5197KWy7YzAs2raSUGrRASObMaj1gcRLphxJsROnSYDCJtu+SVEGibfogP50d8tEYajU59Ntk1ZCaqaZBCA27dz3I29/yFn7jxb9C4ZSXvOylvPfv3s+3v/1tTj75ZMbGltLrzWOt5awzzmbztpPYvGkDX/mXr3LjjTfw4he/mLLs4Usl+sDkzDSZGEKEVttBzBGNhFAzPDqCSkow+Jlooh4/jhfTicJ8qHB4bpvuctaSCf75wd1cuHIxq9ttrLU4ySmrHsYmbsLACxX+/dUzpPojHWWa+EyRFBmZa0YtHustnkQM0aYBKyp4M0hUCxTGElQZdUKFUkWLWsX4gR20RSQgxiBqURHmak/LWKoY0sOpSjAFMfiEKSgY26aKiWuhKgSJmBq8eHLJkkOZsfRixNh0fTEeow7ECDaP1N5QWw9qKICeRggVhYUQDVGTiU/QgNeE57RFqLKAixlzJtBSk/Q7ETKpwBfUNhDrCGIQjQsxmU4MVQzkRuj7lK36+ies5fUXrkuwtMJ1V1xMT+YpJeKkoK+G4D1WDD4Eghj62uelp20kmsgFy1ekAKtosHg0pPoxGWU/FlxAjo0Uuj44yqRKWkTTpmjhYx/6MHkr46/f8Te02kPcftsPOPvcc6jrGiuGsizJsgJjAtYKq5Yt5fbbb+c5z3kOr3rVq5iamSbLMqxVfB3JXTr+WaOUZQ8RizEZmJzhkUVITHGaUUjh5z/BPtMjIJIl3UoazdeoYITcGjCWa/Yf4LI1o7ztxjmetj7jgXKSigpVTVEFmlh8oTm6WGVBX6NiEPEp2jKmqsAm0glok16vlkoCmVhKjc2unh7qIMdXL0oVIwVQRiG3Ql1X4IpEYmtKctSmBLyoGKN4rxSmSasXIEaMMZR1EvlZBN/0FAZMWjimLKYJkzJqKUNMgeE+cStqCZhoU8aNMWgNXj0ZDk+dqPQuBUL1MeReicYm78v0LjEx/R4vFuMjrbxFz1fkWTKzQTNqIuojmAwZxFKS7p+TJCGo1DPsLN0AViwShCpIAx8qBR1CELxqk8mTqrXcFFSxRyEZf/KEE9PErcoUYKUGQ9HYLNrjZA+PjSVk8PmKYUETMzgW+joizrBt64nECAeOHOZv3/FaXvva13Jg3340RnxMaMr09DTXf+c7vOc972Hjxo288U/fxOrVq3nSky5i7/49vOxlL2PbKdsItSfPW3S7XbIsa+ajpID50cUURU4MSQ9lmj7IT3IB+bE7W9LQpo8fydM0pYHvnury7HVrWN+ZwJpJ8ixj3/6cbWMFVYNrD6oOA00sZuIRDN64agPrcgwl1oYKbZHma7OLDlAaklzcIhTWkKlPZs/RUAZAk/2gZC2EBJXamHB8OW5ypxCkRiBoEkJhTGKcWkwTVJ3+TkgLywCCNnLM8t820PSgeWqbr21xyXNIDBYFm9TFYpL4LFppGLpJNTzIBh7YPmZiUmVmUjxAZk2ix5OTx3TPQnMejpiFXJLBOzQm/Xy6Z45+3eyokjX6inQ/g0rT2lKMaTgdzQdf4RHrGi1GaMSoeYPN/XuLxWNj8YB0XBFzzD1/IOuHRDMXUW657VYO7NuPr/q8973v5ZzHnc/4ksX4GMjzHO89S5cuZdHoGOPj46zfuIGlS5eyceNG1qxZxfj4OP1+P4njjFKXfdrtghg9NhqMMSweX8z4+HBzHcceyOMjYv9TMlG1gV6PT7pKF2oQUZZ1HGsXD3Hj3iM8ec0qvnb/Ib66bz9PXLcKhyS+QQN5qdhGuv/wsktNgnYHnrFBJfUuBpKD9L8e5jMxiGeIZhCaIeycm+fFX7iFt9z0EJ+85xC3HZ3BxS5gqEnMvkBcWKwW3kuEkHwFGMRm5SZpHgKJmq0kUyU0xRYmqwFJ4d8DBujChyn4xuq/T0AkYE3AIYgGjG8WoaiEGMkEPB4T0k6X2HqaekEkVChouhqvPonmbPLMHKAEg+PggNospL8LIb2usclvheb9J6PmdP3SEMM8HrW6YKcA6eFpqcOFJOTzxmDVY02a7NHUx2BbSZZ7SjiOTfyzPTRKc2RhoWpeCIaPqU/13Gc/Gx8Dv/T8X+HlL385U5MHyfMWRVHg8pzRRRMcPXqEd7/nH9i+Yzuf/OQnGV40yqZNm7jrrrv4/s0/oDM8TFmWCeEUQ/AC1pC1WyxfvYLO6FhKP2w2umOb78D6godbhj5K48fPhVFLlIDRgZioodIqZDZFRs4j3LBvkudtW82DB6b49RM3snooObFbLaj8DIUZaRaP1D/Q4yZ6+n2pDDANQSft7An9iaoYEXITaNmmRJY6/WBwRJukzjHAp/bOwu4ZcCWv2ryOv3nqyc0yYMiyiK0dzjVsHZMsFAM1VtvU0sPHFpkYajwdlyXqvtQohkBFFgswMRGntMnTs/Uxft2gXBuERwkgHiSj75W2MwR8ggJtpAyWOqZGpYmGdHLRhriktJrohZZErFcyp/SqPoXJsNYTyeib0PBomvt53EKWmyzZ4AWBPNLGQaypraBa0yGjpyUZBdEEiJYgVcOm1WTma+pkAGWUImZkFmiOjHnM6WcVBIuob3A6CwjRmkbj9LM7jEkL4qDykCYITFUxFuqy5KMf/TB5nvPyl76Y51z9AvYd2Mu3v/1tJg8f4efOezxXXnUFq1et5bWvfS3tLOf3/+APufSSi6n6Ne98+9v5hV94Cqeffjp5q40VR1XWiJVkCSkQYkQDqZFrNFEf4rHqN8aIuEFo2H8yU+XUlXg4AzXBsEIdPJmDG+7fw1M2LWFlu82qtRn9sks3wj2HjnLm4uWoczg7cDVzWGlK8oburaqIESQoAYs1mjJem9XWEPEIgidGS601JhjyvOYHh6fZOjHCuMtQ7WHsPGIzxBped9EGwFOpx9RCnZcYY6mqGpzFaMBrRR7byU3LZqkakIARQz/0aJkU1oCmDBBtKpXB5xNxhOBRm/oNmQixskieuC25WKrYZdecctGnr+OpK5fw9iduYaSdk2ueApgkILVDG+GaMGAYpganiQZsyW998146ecaEjawc6vCLW5extkiVDKRympiMoQf5w2gka3xWYkgamntnPGtHMiosxla0YpZ8UCVioyWz2SAnithMVp+WEEJUZuqSISt4G5N1YXBkEvFGyUUIXlEXUG94dKbxT3Zos4ElkmPzfVOhdooWq1evZdH4Yr56zTfZ/9Ae5suK9lAHYwxPvfwynM2YKWuuu/6blP3AP3/8o6xdv45NGzey44HtDLU7qCquQShbnYy6jEnvEgesbZOU0IN52DyS8bgK5Eev+dEYj8APpInX03SOH8j5VZUcw86yIhsSLhxZwYFY0pZAlQVu2T7FPYenOXlJi1bZonI1o5rK/EhDJRHfXFJcoLf/6Pk5NphghvDdw9P8l6/v4pWnLOYJG1di+p5LPnUT1kVec86JPGXtCHE+x41HfM/RabUhVmS0iEWFrXKOhJKVhaVLQGrL/dM177rpPt7x5FOQ6HGmJA+Gt96xm3d+fz8dE2lLTktK8swynBcMFxFxwohLZs1FHskzx7gtGLND2CIyopGsyFnuoBDldd+9j/3Tyj/N7uaByS7fes4TU7/DpET3wkGlLNg52qZBbJpGcj8WfHrnPqb7BoLlhLE+l25cTmh51LgkwiKQmYxKIw6bIFag0cPRQ3CS8/TP38CKds5bnnoC53YW0xNPEUCaaIDoj+1g1kApjiGBXgUhFzJv+cQD+3nulsX0otIiQ5q+gDYyXakfG4tHUhQ3vJZBcSnSUPSFKkRe9ju/zZ//2ZtZuWo569atJ0ble9/7Hr3+LKNDHbzCUGG47ebb+e53v8vLf/slZK7g1tu+z1vf+lb+6LV/wrXXXstll12GNY6yCuSFpSxTvnA60saHHVu0aRIeX2ksWC0+ir2QR5ALc4yMpMSHNVU9gSXO8bSlExwN8wz7iK8tO3Yqt89O8VunrkV8QT0U6UgyIPI0q+cASycZCimRIAZDIIrBNrGqKTdWcRJpmRY/ODLLr1y7n7XXP0h7zFLiiLXnT7+3HR9WpiZlXIQdOUIWI1NljivmsX6Y3FU8+Z+/hQNecvIWrti4hN+94QfccDjy6fd+mz84axOvPGc1/RD57W2beOmJ6yhDzXRd09eK0htiEKbrQEQ51I/M1sp85RHfZboqmQnzVF04Evv820Mz7O5WoBVbJsZ5wnjGnn6bfaHPdfsPcu6qESQUjVhAkCALbNzB4kFDnbEm0rYZ02rBz/L+yx7H6pYDUyB1Os4ZEWJIdogLR8TGFNsYwxCeoBUv3LSc19+1nwv+8WY+ccXJXLVxNcZCGSMabWJBihCM4lTpCJQI+3vz/PebdvP+O+5nthzh9kNL+MPHb0ErQ94KZJrMgOW4rvvPeh/kWLTlsdzbwVtKgWBJPnjNtddhXU5dKXkuPOc5z6JflYSgqE1V3OjoKGPjQ3z2s59l28knsXrlCp511TPJihZnnnl2EogIFLml8j5FocQBwfLhw6R4o4XjigjEcNxi9yiNR8BEbWoQsQs7ysLkLBSvLfZOdfnQXbt46dkb+J+3/ZCzR1r82klbmO710HyOZWUHaadGZCb+YW5j0KyY6WyQsnHVNnhEGlnz/9vUSN7HeuGdT9nEszau4eQPXsvdXcsfSq8cAAAgAElEQVS5i4Z4+sZVvP7Gvawpuhz0GZWPnPfJf+PUiRW8+XGrOXnpGCPFCHfOzfHnt+3mh7PTlPUSjO5mzo7wF9+9iReevpQlRU5LIHoh6+SUByo2LF1Brn2cldTkbZpoYgJfvPcgO7sZrzxlHUM2pbqpOF70lbv44P0PgTr+21nr+fVTVzIfoSWWqk62f04SFCrh2M6WeCiDe5P+kBHIcouzHm9JubdZjonpfoo2EYgM5Pa68PuMpnO0EyVzliduWQ437YK28MZb9vPszSuJQRELLYG2U0DxwbN9JvKVg/u5ZvtRrtk/ja2hbzNaeclf3TrFke7d/M2lp6DqUkPWN5M6XcGPO93+U47j+wrHP5yqSlUrSmTjxo3UQTA20q9g9ZoN6d+j4kNE6j6/8uu/ypve9Gd8+rOf4vbbb2eu2+Nzn/sCZd1l6dKlLF2+BF/blBEdE1Zo7TFs8timcFyfjbQZp23eoiaF1euPj5f8/xqPiAfy7xWjg+bNsI/cVynTPc+OI/P05mHrpmF6fpZbDsyxblmbJe2INs20OsQFBeHChxKPeYOoRI73XEBi8qoISitrETUn+BIbUqh1bTMkeJaMFXzg9l0gjkOhpvIVL/3WvRwh48uHZvnyZ2/lo5duI/OBUpUTh4T/efHJ/P1de/ivN2RUWnHNCy5lqTM4MkTB2UTVf9n193D7dM3WRY5nrF7NmlEIYsltC+8rXnXN3Rx1wl/eupdf27acNzzuBHyoiNYnQx7RlAGc8FaqqEQXsDEhOlYMgXRfRBpH+uMaYRoFjKEwjmg8+ApnXBIoasA2/Qmvx8yYB7Bj0GSZ4DVSiAEC5413GBvJmS573HX0MEdmlHYHvnjPbm45JNw/M8NkCXdMTTIVIyYmMWIwgaA5hiE2jOasGGtxsHa87bvbed3jN1PWSsg8qEuaGY0PgxwfiyPN4aaBqYEgYMUs8DOiQCYW0xnixG2nAnD1s5/HL/3yCxvSXaQKEQkVzmREkgwiNg+JqtLtzVDOd9PvNjA2tozMWXzUBTTQRk2VbLCINQiPTvP6EaIwg/XsWA/Ex8iIttnHHEWAfbMVc3VEreHevV3unI6syCMbW2P4GGlhMRpxTbAU8dj5LZqIhMH3ssBITRV8koQbEcpYYY0SjCUaCxroGINSsTLL+d1zV/N3d81SuAoB/uq8zXz8Q7vxoc/jVo5yybpFvO3GXQz7giJrAcncxzmhL44V7RyRFq4JiY4xI9QlnSxn/2yX/bMzXLlmFVuXLKY3F9lX9SmARSNtDh4u2ennOTRTARFnC9AaQgZWaRkBG7C0k5+pelRNg9gm8tlgnR4QhCBNQtscI5NS04EMjpEGazK8j7ScofQlHVMwp55h46hQjCqlejqSfF1MNBSZ4fFLM76yo2Lx8DCLR5WKNpuXjFHkwpYlhqxoUVWLuG5Pn/ff/QC1szxv8zjvfNwpTAzlJLJkZKp2GFNR4ujkgaA22Snq4Jj62F5ABiiNwVJr0iBpgyiKyEKHz4lD8hynaUo07AEsiTUsNiNoUqUnwLMhq2mkX/eZ71Zo6KEqTEysSNwoa1KjO+0vIIYkWXr0nOB+/GQ60QUV/6AHElEyMcRY0a1qPrx9H+0OrB1pM+wMW5aPst8fxmIYyYSZ2hJtypRVDVgytMmJgYgNjUyvQR6kIYQkAk/EGptIYSINEcxhNekzul6RmFFLMsqpel1krE1fFHKHLdoEnef8ZYsYNZGOOPpmjspn0ICOVQhEm0Rlri14dSnfxHrEOGyWgRMIyokTHc4cL9AJCzqKqvD+23ZzT16zaqzgXU8+HShBA1FzsD3wOd3UOk7s2aiJQWsFqxHFoJJ4IIpN3ksqzVFIE41GFSNKVA+qaAzJWJdAyxm8RFpOqKMylikh1LQtTGqkFWyzoxksydDp9edsw8sD/OZJyyijBe85cckY2yYMIhMUBObVMlPthXuE4CvGixGWjuQQEgfHkzOa1ZQh9bDqIEkqoOAsVDEeFwH92BweDxwHUsbUO0qmT+A1YLVJYAwlNUnxDU2FYhSxGbZZTvS432VsIlLmxlKJx7sWEnyaBzHRG9Q2QsaGLGgyg22g5kdjPIIKJEUlyoCT2fATogrqDCvyYY72A/iMt33vHkZti1FnMbVQySAa02PCEFYCyLEbaDWRy7wN4BuDIYTCJgGYaxaM5A856DwHVMxAk0cmJoVf+bTIYBUN0DZKLs2iE9NNRRySkR5WBHCN7wgQE75OSF7DdZM3YUxEqTBSpQjOmH4OtfRCTcsY5ohkzhC8Aj0gHS9qUwEZKei3BhxBk67ERNeQ0JRAoBBLaSJ5FJSY+geNfkgjYJNJcmYdtXFETRJ9J9Lk54KxjqjCfB0xxmHU0I6eMgZGjEsEMeMIIXDK8mE+cdlpZNbhxOCtwREQk3ZCMYa8sWMkBqzLyUwJmmjVEUVqYVqSu1qhBmOPEQ57tS5ocR7LI8MR1Kc5JInQb1Qb6nvAKUxOHSH0PbXvYcioQ7XAWBYsUVMIlWrA2mzh4R+kNXr1iU9jBGMc00cOp+OMgkjanHPTwrZycBYV/6i930eoxqVZFtOOLWoSqUaVbvDM1TWLXMbvnruRT/5gB8a0iRlk3qBGyBpJfpRjGg0hCd6cyELDSFVxBKK4VMYnD7dUBcUMoeFJiBJD47eRVHqoqVBvQUFcje21MSJYB8YnaT0xObsnL5Lk05oeqtRkHPiyOgHR2OiAMo52PTEka7t/uHsn1x48TC+Cw7Gsbdk1o9S+h3NDpAUDEIs1kDaoLDmHYXAIVhQIhGgTjEuk9qnaiKIpTNmm189ihoRkI5j6KRkY2/Bj0kQriI3zaY2q8LJr7uBZJyzngvXLaakwnBm6IWAyj/EZLROwYsEmM2sVS2mVdk1CDEIFWBDI8RAsQbK0QEYl04xerXzxoV28+ksP8bXnbmLj6CIqZ8hwxKgLdgKP9UVkwMMQiZhgFyzCxEPMhcn9e5mf7WJslu5tg+hoTOxgIdlJxhgJCiFUx5q1zXOWfqFpFiiYnp8jCYMb9jFQ2B5DxTg5BdL08B6N8eNT2Qe0Zj3246G5uqDK3rLkkmVLaQHLigrrWhB6jTdETVUHVAOVTVJv0dgsSA2lmh/FrWWBuKOa4KsBahPELjRg7YK+T1OFYYReSLu8qtLOhRAjwadLd2rBRBySGrXxmHvWoN9kTUrRi01/IU2OkM64Lp1mn7JuFc89YTnPWrOCC5eOsmXY0RqOZK6NCwm9QFLWbzsTstxAXiZLBGJjoqwYE8lthYSU22JsIJeMaAS1SfxmJC1aZJBgmoJQlRADGlIPxSP08VRqKGOfD921jw/df5Bf+/p9fH/fUaKJzNRgnZL7yLxTDtfK8n/6Oq+7/k66ZcSilPN97pot2TsXONA3zMeKqVqpRKCtScIvRfrqApPlNL/xpfvY645w3sfu5oE5j4lQ12n386K0/o+03z87I2LS3DYpkW/AWvVlxXyvRF2kCh7vPSFGvPep7gwBH5LotK5LiKnKjCFtjBo9kOJQDAmeTYkGYeG1BwBEJC3uAynD/9Ny+T9mPCJHMtU4EFc8XLSjhqPdHqesEe47JEzVbTZ1cvpGWMQMiyeWUNsM5wXx6QGKkpQjcRCT2bxOILllxYZMFVUXuCIGUNGmUgkokRg9DB52m/JmorFAwErBqDMU1hDVotalxDpiEgF6SeeUpqJSjRhrCLGmLRlRFHWCiQHICHVOjCWGklOWDHPekjFiQ283YvjAD/Zyz1yXeWeIZBj6iQIfHbVmSDSUfc/1u+a4Z67Pr2xbRKCNxpL7p/r8/QP3YWPBWLtkVNqM50M4E8gzZUlekDnYNDREGSuciVS08f2SbgiMN0iLNyU7jwq/f92dkAnTVZenfula3nXxWTxv83KOzMABHxmTwAd/uJepvvDWH+zgfT88wL8+43RuO9Lnfz2wj26VMRf6kOfM9YQjc3NIqbisxUe37+aWI4bDvcnkhB8DzGV0s1l+45pb+cbVZyG2lTxWJTmdPbbrjzRE07E6isE1x06xwuyhKTJr8V7JcksIqerAFoiGBiBQME3/o6lO0vOVHtWFasQkmF4l+dmmTdY0uEYSRTq1qHn0EBh4RDyQYzCVNpXI4IxWZ8r60Q4PzXiwFbfuPsLZG0ZxleesVWsSh6TuYbN2YyaUJzo6iZ6eVuoELVpSL0NVj/FEFjJ0G0xmcG5cgCjMgulvUJjseXCRbh04a7EyFxMUW5ch9TeAwMD9bDB8QjVUserpxoD38MUdD1GJMJwVXLVtCb8kK1nSsbQEbp+coVTFWVgilledv4VXR2Vdp42RPr3aYIzn/CUF+dbFFO3AxvFRnv/N77PrSGT77EbecPY6ghXWjrV49toVyWy6Sr4pM7GPjxkeZe98n+m6Ys/RLlXfpoUvm+bDu+ZYfOh+VJSeWFyp9EKPq7eeQEWXqDl1Ffj8/Ye5fM1yDvV73H14ntoaaqoEA7icaCw+Op6zaSUvPHE1dQBnHFFLFMPf37GLV1x7P7WZ57kbV/M/LlqPxk1M+z617/xv7t486razrvP8/J5h73POO945997kZiIJgYQ5MpcDiEgXC1ss0VZRaXU5W+XUS1uFoqwqp1VSunRZ1XaJ3Q6UA1KKWtIICsgUCEQIQ0JCyHxzh/e+0zln7/08z6//+D3nvTeAZSddRCvPWlkL7r3vec8+Z+9n+P2+38+XHZ1C51ke2UTt1dUjoUJ6qAXisTmsVVuDLfb8W/2QmU6nRp/xHs2lMj88XhxDrtYDPOUCGYO9xkO5OXCBo/0CI93i3xn+qmGg0GarT36hdMCPbFNZQF1Baht3cQGhdxyeBO7b9Fy3b4nrj00IMkIYUDfH50xp19A0t8lEc52MynkNCEC93Ow8ztmZw6udx7WYMEoBVzLZexZZNXtnjzzg1LGZC4RCpuWqA2NCdsb+hGrqsqKEXqAzKVI/6qI439LpwHJsGTVLuKGwlae4osxd4hMbPXee7dhKnnO759jJme1+hIRTRkYbRgwibHXKThp4sN+l8ZGYlN//+BabOoAP/OubPsNWGvj5Zz+ONgjPPLafeVJcUEKqW1UPxRkjBXWMQ+Kn//YznM0J5it8yxPWuXb/fpYWwB/v6Ysy9kIoHtwcNLCZe9IAJ/bv4+lHVskUbnnwAD9z891QJnznNas8af8aIZggKrqGTnsaMAC1NzaIqOC90bHUwZpfRf2cgzJCJ/bJqmbmLqApQa17PbbQQp9vqIV4VT+XCfKEkgdKKZbDUwoa4ODhw4xGYxIQspXHxD1UoHbhcb4UK7Jvbmywvb1tR52cOXLkiGEash2vtQjee6Rp8LU08IXqnj+yIwwXQHLPlwzYyT1BAk88MOaq5WMQPW6nY0sHnHO89Z6TrPvTPPPYIVQd6hZisfOv77WQ1Q4wrroNXap1jwxObNtGdogPuOxJWtmcWK0Ab+FG086qvS09l4wvZpeEFk+hM4eoOlwOuD1GSSBQj2XYzmrVNXhxvOTiNWIUHAesNVkGBjwjp1VheSnzMmMsjtPTE8wVji0He+DFgURe+77b+Vcf+BTJeV73zGv4gacd4+R2oLgZh9qRuZyLMFejqcmgtsMoICrEBC4Kcwb7MIA+eUg75F4YifldOmkIuba9k7BDweXGdlUxEhGyM6ptoy3IOUgJ/JyiRwkeDIY8ME2FSQwmhANcalCf0NxA6sEtsoxniGtJKTFzMC6OEgr0BkTC25H0sT9/KKpirtlsRrdSCilbYdQVmyhGoYU20JGQEsjOBBxahJTz3iRikvkLoFXB23NTg9I1F8bjlb3aoBSlODNtdiUZciH3X7DLffg6EDJOHtrN31NJekeaD8wRxEfeevu9kJQb9nl++sZPc8Vqy1NPHCN7oUHOd07EsWAr5HpESthDpOJN95AN3qM54ySQXU9OipOBUEbccXaXDy9tslMyuMA0z7lnZwecULLnKYfHoImODi8ti2lPK6djUQMZtHAhn3WmQqugPpB04OzuwIv/9KN8+dEJTz28wnMuWufAeIIUSDRsyQ7f9LZP8q77NnnF447yqicc4PlHDzFNhSRKQqFkks4B2L+SSTlWGrrxP0L2FKnsUbWQ7aJKCkqk4FME7xA8TgZKLPgYTDkgxhNR9QTsOiLUo2Gh5ED2iZw9bRV/WTvaujpeDa0oboYS+eM77uT40gpPPrLEkksMLoMGmrG13Cnw0Y1dvu+vb+cbnnCQLz2yytGV1lrJ6pA6wfjKNVkcHR+zo3ZgMg4pxmpBQSg0IdDlGc41uOj2coMDkBOIy3v1i8WOAqo4bcGVwcK0i/aIRBNi5gFxdnhPOuC1saYEnkwiVNTCF2I8fB1IMRGS0/MWYq+C5kI7KLtBWN9p+f277+H05ga3bxVufrCwM/eENWN1SBaII2N2OI9H92IPg3N0e5OFFVqliO1YioAEkmSCNszYQiXQhxm3zDZJD0Z2smkstueJO3fn+AQuwlMPrjNPiUZbptpXq3TB1fZtBW9Y25EaJYhRLpwY1WzkzK9y8/0PcPOZJUI5zUtOHMAPHWfLJqd1xMg5bj87Y14yr//Y3fzJp+7j1He8CC8mECNHjBvS2KQgwf7OZcYIQ/FkLURXSAs2Uqmis0EoITCEwrjWhlyVHKkWUzxW5WcL9HnAmrpU0Zm5fR1C8MaHHeEqrwPILQZFMcK8iPKzHzzFhx+4hXZtxDOXR2x0I8iZfrvw7pPneO2H7+QNH7uDj2/N+av7HgAGfvr51/ED1xzHjWyyMR+NUiTjH+NFkIX1wNzT3oh79eHvhsw4RLIHyR7JnugWKmJFi4Oqb7qQ4bE4zqiq7bBrwdXQD1ZTsfZvIUpDJiESqv7EHvF/PDWQRbv0vICDXGW66h2buz1v/vSdvOlTZ7lyLbIUMpevj/nEA5skWlwCN4l1wnAVh6h7O5DFccgrDGJytcr6qb9O8cXaUqGDYeiJCi+76DhfdfUx/u9b7uGUFnLjeXB3F42R69fH7G8Sd/WOaekQrKOBWClEM0RMl9GrtVwjHhW/52NwzuGko/Vm8vMSObFceONLrzWVoaq16RG+9y238Cu33sPapOHNX/EMkJ7GRUZayWluF9VkTIlkn5s6YSgVFqNKqfaz8zeS+Vu01oNsNS/k4iEJjnDBjagkbGfnipJc3qs/+AJZCl5LpYibkA4FP5rTegU1U2QjM1ZaB81A6Rp+9auvxyXHjKtRHLt5F8ktyyHw4+++jSQKEnjK6gpZrL7kpRowRfYKi4/lIVRei1TEknicOHxo2X9wH6HAVHtiMyLVXJ6yaGpiYObF8eXzgYA0J1OcqpJzQUtDSj0xjurPGLumiMnem6pytnEeAPbfazz8CaRorZCxuGq0Fmm8CBs7c7ZDA03mmkNHuHfjJE30gMeJgVcWeDupsvgF73PxoS0i+mQv3qE2oqoOxFR9luYmIUA2FSy6TTedwcyzr3hu2upwYcoLj16B0LDddzAUXDNmVDs4iQ513rbm2C/2LOBGA0q095KLQbl8ATchhy3mfglfDM89cw6fZzR+mc4DTWDcwrOOrwDKXO0/yhyaQK5B277CefucDOy86Ghl6vuwSUPVAp19RTHijNMBWOuueiCoOyfTzFh9yRdjle6VMIugCL5kgy6L+XMaaQiuAVFyl/mXf3s7vSg+rDKkxEXtEvv3edIwkFxgkJaYA+tLiZ+4MZBz4Yevu4Lnn1imUWi6EfMWpB7B9fPwXR5ro9TvwbMQftk9HaKwtnoAgMnCKJqV4itxrzJwLiS27dHFLpxEnGd15SArS+u2+AHONfZY+iq2FMNSilMjw+39/H9/R+4jYKJSc2FgsV9wLGhXtgoOw5yxBIZ+jnMTpM+oDyaMUjWGghhdK5TzO5A9OLGyN/tKNdPtpaFzfks3d2I1AYVGM5Qxr3/x0/jAK5/Cd1x3iJPzjlWd8A3XHwSEofSghZwzI98CiUHaC7o4Vb5O5Zx6xQlGJfdWyLR/l/F52dLpK0dYKTgCpSSG7HFJ8a412Tq2hWwXHIKcTSW795kWgngzDcpitbaxaGEvOLGLz4jiKdHUqXhh0MXxoJ6hEROfIbWybzdkXnxXIlVBnKztV4Q+Ox4cdnn1+z7JVb/3TsahYYzgpIAPOF/h1d4TvUGhWw9Db/5fVzIaCqLBbv7YowsvAOajeuwPrYsh1V5Rr31hUpCM+vNBaHZPAXpeFHnh+OwdSEbx3kNoCI23TphUAtkiIU/PP9YL+NcXajxCbeBCt29Dq8jLizd2pzf82sY8sxYgOiO2CwVyRorJ2BvnyZrs9WTRyrXIg8++Zn9Bt2dRbV6JDd/3pEt45pEJX3zxEVQKzzq6giPyP//l+8k58+qnX8XlyxOKOh5MM3BCEzKjWFf6IRMG2KVnd0hs9zN6b8XJfj4gI/P/GAfFMS8DlIGscM90zFN+9z2sToRpB2M8kybx8dMDGh1DEqABpSa4WUo9zjJkwOGcIsnMb17d57Q5FzeVwYRscvXYg9+XCnguFkCEDoDViixHpra2FxJodQQxN7VQhXZZSINNQpmBX7zpJE0becmVK/zoUy/hj2/7WyQEZBgQhQEYxIROIlb8FW9Eek8AHKNgRPe583vdLFQvZFE9ZoeImMCL89cNdUftqghSrYtZcjr/rDsMQP2QIvNDd2xm7bDupa/PTOOD2Tt0QUV76Hsx0eUX7nofAQ9kQS5d2Osx6lERpuxCnlPKKuRT3Dvf4Rlr+3BemTFw16xnpxQOFGiCMuzZjM9feJbykF2uoki+QD8gBV8c2cHV68u89hmPJ+VCgyAMpBK4bfcUf3zLGU7sW+Ybr7iIUhJOHJ96YArOMdDUh2FCdoVu0rN5ruVb3/4Re/iTQxIkCXS50NQqeJGMZMBbPeJgA+/72hsYhZat+cB0ELwmvvOdH+NN99zF6alybpZZX+oZSWBVgKAwNMzq5aRBTOqutjv4e4uMUijFQbAsYqQDYKY1uU8/S0dAPW1Wk2Cm4CsAKZPAB955z1ljk3SJuBa5LAr/6Z88AfB0oaCzHoKnjVaTCiVQvIAKxSeCGg/Xi0PcnExAsciN3Junp0qGHvMjS7a2NucDp1RNUKZFINj9a5m3Tf0pO3Y4nFkXcgb1hKDkYkbSBRsm51x3GFY0z2JJBJ9v9/Jo0N8efhfG1q49vB6w52UJ2hL8mHOzezm0vMQsd7RR2e8njKXw/IvWuXgcrXBafG03VpWcOkQ8HugZWHA/wOF9IRULzkE9iUJQa2lpbQWLywy5ZV52eOVffhJGkZ979mX8X7d+hr8603Pn5i637e7gXGGUZ/yTEwcI/YyZQEhj9q/M+M9f/jxe99G7+fEbP236ktwTZBmolXTnmA0JfKF1Yy5ZEiKN5bJ4h/fCJLS84sojPPngGuoS7zh9lkNbmbEs88b7zli7FNv5gE3I3llodnSfvQJ97ihYrgvq6ctA62BeCp0qQxGaSoh3RCNZaaxF07oi1aJaX6B1kVnX8bqP3Q0uQBs54lre8TXXsS825rfRhsErSE/fFVzrUToyEZeteJwx3U3WgC/Rji32oRkcG+tMfL6b/LE2XPFonZgvTIlzvj7Q2ZM0kbs50/kccFASxQmTyTKjdkyxIgpDsm6LqyHegtriUY+6tikVXPE4J5Rix+9HczwCHYjBfViokuWCM1ZO9Drn2uVVrr3kEO9+YIsbT57hx552iNc013FgnAiloaOKYVSQvdXdlIuIR8SToZKvC1lDPScqpUYWKBmvDipaIGsguB3e8PF7ueMzPX/z8ut4xsVHkJz44QBf91//lnt3hJ0+8kcvu4GrlhvwhZOzOS47ln1AZMYKPW1IbMrAMIzRksjOlH1d6rnqwBJb3/EVLPuWpLsElxH1+GhT62wofO3jj5C6hEbhnfds8z1v/RQf2jwDZR3aHvqG/dG2t07sRmm9p9cB//cQM1KxIyBOIDiGmQAjZrkYb9P1BB1RKqtF62Sv2OeXJRMlVq1LYtx4XnnFQX725k/jo/D7L3oyR8IayADqUDqL+yyOLJ7Wz+nyiIiZ6IRCUEfrAzvAgHFZMlYUthamw6kJnFz5wqD1/rGMLIlAtLS4vfqFUrKR1PthzukH7qekTHGWumiFUmXzzFlGayscOXgErTxTwLwuks3y4QuaLIbVqaEdxBVUjQr/aI9HyAO5YAeiNqmIgmvgutWLuPjwFm+/6RSvuG4f2+cC0k9oJxvABNcGiiZiMYCP1gOaKzXxC4BCJDDIwhNTCEkq88CTZcDnhsENxBJQUXIRvIv86e1T/vAVV/P0Q/uZ54FJHHFulvmfrjnCkcky3339xVy6FiipoejAsmTW1ya8/OojKC1H2/288OJNvmjfAS4/1NQipGMAxqHFrEqJuXRocgw5EIPQlUyTwYdAXxJt29IVeOGJffzNNzyNL/mjD/P++zZgNga/w5dffhRIdOKY1KBrL4uE+//G549nkMKgyuZscYzJFFWyKwiRgUTrGrL0+NzU/YKnoDQ0zEvHyDWk4vAu86onneDnbnmAV1xxMdcfXmVwvXEtKAzDgM4zNA3f+7aPkkeBNe3oKKgYiUymHZvTDkoHs4NISUQcvZT6OlYs9MX9A9zij+6IGhlkILhgsR4ICwuhOtg+e44+z2nCqMKgghWeB8F7R96ZcTLfy/HDJ0hBDSpVsIVVC8EJJ8+eJcaW5eVlgvdoNvbtZzNaH43x8IuoC8ObsNiL7Gnt+yGT8Uxmwg0n1hh2Ik1Z4RwD7dDy5ydPcvXSfo6uJ5oYySq1DmLydC2+1lP1fBsSm7QWKXVWl/IUb3hFG0KgMNXIb774OsajBu2UScwMOTMaZ77u0mN804nM4BKzEhi3mUE9H3/FC6FJeO9J6vniqye86PInkaMnl4S4iNY6yG5WJiI2mRAZYqHNYjeKevqSaOr3t5MNMTDPgaTKSy/ez6c2Op53ZIXX3CqQgJ4AACAASURBVHAZV+1fYpohhEBXMsF7Oh0MM/DfGAt79pZmui7BEGFIuDwwV2W5BESSEboXK6AUtFTSPUKsWoNee1ptObg04meedpRL19YpLqLaMQBRlFwcMhpQCr/ywifgHUy8kDTi6cE3vP/UWX737puYjcbMUzGqVhIaLOGv5Pw53YTH6lDBhJJeEB3AjVAdEAJDN2faTQmhsWaCeIITRAISKyzGeXZ3tpit7BJXRvZs+FKPvsL27jaz3Sm9zAgBJsv7qphMKsDq0R2PsIgqn/NnYBNJEwd2hmUOTjpSn9mWns9sbHHjmTNcujziilWH92topYPFRcoXVsfQ+pZy5eVA7bqc/23WyswZ9XUyAzogquKDMOiUNiyxnTPr3tMXTyIzx+HSiBh6Um6ITphGy6KRonjJBA0MBEpOBIl7cvuUFecycxVCiZQkFJTssz1ktV+fciGL4rEjxUgcjVd+6BmP458/40pcGdgpSiTgfU+vBZeF5Ph7Jw+A4E0rsOSVX3r+5Sw1awQPz7v0IPudp7jBEIOie+pT09noXkvcS033CxFNypIq3/70xyP0CIW586wCRQtr45ar8xG+6KhnyQu9C8x0iqD0KL4MrKpAA8d94MpDy3iF5GonbUFR+zyiqMfiUC2oz3ak8AHN1ll0opw9fcr6l0VIJeGCZxA7kmYxA6gOiVETObd5lkMrF1NKddP6wtbGWaZbm/gyoK7h7IOnaOMyhFCZqf+DTCALaKwFYp/PnUja4N0OjUucmjo+/MADpL7laasr/Nmn7+GJKysk39KVGaPQmtBF7CizF+UgVYiTITnZ06yZNLi+BzLOGRTYyttKlEKWgCsFrybUWnaRXBRxiQExn0EsZGkZlYFBgiXNieVvZIGGaA9GrR+Yo9IKYLk4GmAQzK0brIsiUqkkldeKGH0rEiBknAbGFcvogicIREuexWdTDfpU6KWYVPnv+w6KMPKB73zi5UQ3YoeOJbFYzU4jSELxliDlTRl5Xt1YEQZqEKUiDcEHloujuDFFzdqf1Ziyb3vpkwkxoAmCdzQoSIOKN0OlwvWHJ5z+xuczcYEpCz6rsVgA2wGJRx7rPpg6XAm2Sc9VQq3CfD6jn0+JsaVPiSa2+Bg5fOAw2UHXdWyePkvxiT715GIwblMuA6XQtmM22cBpAFWGLJzbfJADB479g13rw3fjLlY0Pncnsuxha+54y2fuYb8f8eSjq/zyu+7hqusPMi2F6AsudDSDtRXJoAyIP2+mk5pbsjdzqJr/RqzyXNQ6EIhBxIKjlu0cIYP6iBZb/WYl03hPSaZQsADrgJaBToMZ0moPPeNIDMyLQwQ8nlIyzlnrE7DOzwWqnFwsIS9hEZCCQ0oN6hZb7WcXGCEtpDqgWpib6Jnoas/CA6WK5nTv/wL11LiYABKAsNPbltYQGy3b5D2puMORnVLEUWptKVeVbVSshlUCmiOdmtcoU+puoXqBxDH3QggNPaXaFQpuKPROCdlZ5ogat9arZ4b92VAcQR394JBgD5LWz+1z2sx14tML/u6Cu21P2yCyuCE+//i7djh/159L/X4+uzO09+8XUaAPZ9Sf2bs+OV8ITaknBHNER2+h8gcOHaUQaILgJKIHlI2T9xNday7cvsP7CQFIKjTtmAPrB9jc3CT1A9HDsDtDDkrFIz60BvJo7PgekRIV6hdeVyCRGjHgBlwMLMuE23a2GWuk8Y5jrmE1BG48tcW956YEL3TePCcJg+YshGSLt5Wx9K4i9e+w8/+CVJbLIlVea+sXUij09CCF6AW3SIV3cgESMeOdeXGcWjLegrIWncdL1WJIxntrjzXO7+0MXNVqOK9EtdXBI9AUnFPUFdQZRU1E8M5VFSg4Zz0Wez3Zk647NfNVEFONRpFqerMdSXSO6CE6Z2pQEVyjhGi7IOcqS1Ysq9fV63GueiKw61ZVkiakCEkWqEb7Qn19v0bGKpSiaIloEkgOvJkbfQx7StOcM6qF1jWEaGphLYIrlmsjYke7Gn8DULttUFxB9+wE5+E5CzifkS0Kpf5grsyYhQ0iw96Oag84JaX+HHsT8eIaM7r3umA+ESmLf3f+daG+v3oPZvT8exbZ+/fn/zv/GWpxe6+jqki0nYII9H1vsJ9iyuTJ8irqA7L3vgqj0QgXIrl6WqbTKd6fl8erKpN9+xA8hIiKZygZ0gDVM/aQZ/Ufqw5kQcDQhTmnVKaHKm1XOIty1/09J9dnXHtgwi3dBurGPOVAw+VrB+nLQCxSd2emA7lwpShSqW7OeuqlrlJgN3oppT6MinfWBYrqKEkZhUARk2b7xQ7SKr44sZ1Pr5mxCwyqtDYDIDj7e1eNUPXh1moU9IhZrrFUeucg+2zicXUmMhWHFzuzihQCBnx2dfV3pSDBxEa12kZMhhYoYjwSp4K6Wr2XaG2+emz0TuzI6AZKiuBLfc+gdbV01W3X4sk52eSFq1wRATHb90JlrRcswWb+Ckj9XZRkn4cKISm4wJCF1lmwc3TecJMAxaMuE3AL2awpXkUISH2QFguNILXtuAAQS6kh4tQj64W7W7E8FdxiZa9S/3q/iBpsSfS8BWChMxAxK7tXQ1X6upOxy9aFRdFa3VIQgkkDtHoUAF1YCLRel1+0o/f2Tou3WS3650Vfe07nCq3yPtLnnvHSqHYePbmYTkSKox2PSdvbdaeqe/etq4VAhxBGjjK1EDEtSlLD5LJo1z+K4xEiDR8qZRcxzoOTnqEJ5LTDDVesMvgpB1rlqtF+/mR6CudXyN0OGgKCI6tJswvnASoq7JnnTJnJnuJ1sQMJzm6CUgqtDwwUIgV8xlVXWfEzxI+wE7+Ad5YV6jJNrW+0DKD2CIivU6KvXxqehKkrsxr4MDhH1oSXhqKZKGpHBNGqX1EGUXv9oqgM9edrtLXzpJIIruzVbqi+W4u0SGQZahKepfItxHW2imZK6RHxNDGTVKvms9Qb1FbNRY5w48QerJxrVdoYsOaBCSh9dUWHPXPj4ngDc3AQtKXU61AxRosrBZFEFrHWs0uUpDTOG4YyFxb8kb34DS/V8JdxxFruGsi0NOoNUqRWBxNnmgdvXngWG2UrvHscQl9qOBN2/6gqzsOQxVrLYpGjUmpOrCukbDtHqZNt8gmXbAExpqjpV1z25KpZcSKUUGlgzo6toZi0oDhvaF353KPDed+UaT2oUaVDGWh8g/e+RqLW+7tYBrQ9YwNZPeLcXhA6mI6mYOFhSoeKtebJBjHkUdhxfPZ4+DUQZE9Mtvdgo3UlGtNNey5ZWWU5zzkQ9/HbD9zDJWOHNkLqEyW2qM91pckkzMpOneWDCDM1Bocz0AVgBc4gDq/m2Qgu4uOAZzExDIg6bt2Y86u3zrhlu7AxzZRkdVonkUiyLFufaTyMtCEA0Q20TWDJNwTf4SWzFJZQmbKuLb4VRi4wyh1tM2YcBpxXmlxY9gHXeMYyJzjPCIGQWPINoo5xAO+meC+MJRAa8660YoY2fKmKT/t8I25PxGULnPlbTOnr7GJkANUqhO4vKLwW+0J9zyKxzhfzuZjk3dW7dVgsk3UnVICE2Y3P/3wQI59ZbaVm94Cdt+vUvrdjCOb2RRx9yAQGRH1dbBYOYcFXHUvUQCbTiKCaSSIGQNJktSXnKPW4WSSjxSZoywoaCCWQQq50/cU+wox+uaSaJ1xwwWPdTUcIiSiRPidjiartOFxRnLdFK2ok+2yfqcOuUcWAPVqd4c4mr0GV6Ewnqk7rPexrs9yZkpmxmTJDA2WX1kXmJeOyp7RQkuKCVBUvNL4x46I6VO1IugDDuOLwHpCCp8EO7qbLatV8So/2ePg7EKhu3PPBUnvVYk2Mxy2333aOg5MxRyYDE5e5ZJ2avSK0OTElUpwVThtR8w7U7eniDGz/23JEXFmcS88X3hbb3FkvDE65+YHEr3/yLH9zztH7geXc0vtE4wOFQsyJHaf4rOi04EMguTm+GC08iAIDJdtKS9kme0F1jsu9beUJppEQW/nnOEIxpqrLDakG+GRVoosM2tGIJ2WHi7YSmdPYUt1dGPBDg4ganyN4vHdEBguCIhFcxDlocHu0MdcoboA4gkluiN52Dl5g5KPtzBqHaKLRiPeZINAQkKAEp7R1x9REO4YECiIDrRdaEVo3UJwyFo+XTBSHD4XWGQnfa8FFx8R5HAmPddNasYiKQIv6Hu9avCoej4TzK3XSTBZBMiQvOCziVPHgB1LN/LETgidJNrNZKQx4NAhJMwHPMBSKHwhq4GmkGgzFjruZbIHiCEMuRthXh5SIoiQcQx72lM+qinex7jZDrWmk2q1ThmLwHw2JrsRqsVg4Ym0ic5UlI5gEIIRg97jY85NdweWCeMNbiFSOy15gGuADWa3tLvWZWNjGtPQUiok4iy54WI/6eARMVCswLU5/1pWxXFpxHmTgwZ3CGbY5OFrhxMoKf/ixk9yxfZqvXzpOaQRXNRZUSpfoQ6vlC+m1VEL7AiikUuxmKtD6QsLT+V1++5M7/MQtHU3xdKUnkxm85Wu0UhgUCokoykAhiycMc7tZ9/gLxRSt1JXSeyQN9KEw6jy9VzwdVjhoUOaQFfETnCrZTa1+kL3J34Pd6EmglIQOFVPgPOQeCQ3MHMX3oEIeCqHYjV5KoXMDbRG8OIoKyNxuZFd7LYMg0WxryQ2EekopZUZKNnE6HfBuVNEEYoVBl3BDxruWPteHopjVPLlSt9lKdJ6khZEIfbLY0QUmYBDrJpUyEJMwBCWKxYtmAfWFRhrEJXz2NN6yXp0MtGKoxZEkcC2TAOPiwSViAxMfiV5pNNCGzIpr8A58gFUibYDoE21oGflCZCCQGDVK63taZxPkJLaIeCalEEMgugGvEcJAk8A7Cw1bIlpkRlnslKr8n5oGqMP54zUCBaIXVFO1yhcQc5SrioGRxXZjbcUMgqNZnlDOKZkeVzy7u7usLO9jcUoTK3QhSaoZb0CHKZFVK2gvmhcOylAweJmhD/HhIZ26R3M8ghoIVqEQj1JZBkVRHNt9IJSOHJd49lrmyov2c+VSpJ85Hrd/mZVRYLvv8RKIeQCpegRJLMpfKg51Cclu70vcy8at7bVQHNkV2rzLJzcjP3vzlJXgaEJmnw/kLJV+bTP7GPbOpAvZvUrzd1yhbZ0dwjQJ+1kljXcIMmGHxLgMzFIi+MjOJDOZznF+RPDLIBmNAyV5Ojw+KGFwdNj5OaXEEh2Db1G1ouOSRPpSWI2OafEESXR+xLrPzIbEuCns9CMKu7Rh2Ux3UdjJPapCUfA5MBo7464EIQZhhq1sAWVUoASP75UOTx8SQmESIw0D4gQtLWdKR8yKBvOJNlkZ+5al0KFlRGhAhx5tIkoPgyf6EYOfkosyCmO6YTDPi7PbK7ZCGBpmfU/jAqkEZi6x5UcoHW46ArdLbJaQ3Yz4ROoHEE8vjhFDvddsg55dJmlDKLtktfy9HRqaDFEy83rsi6yibOLd2Dp1wfa2oYCgxBhRJ0xYSBI8ozDQqMc3gvfKWJXWB5yDVe+IYjuJsRQmLjIK1jVbcpGmKQQKbe2uNdHTyEBLi4sDUT1FW+J0m0F6+q3AfNSy5FbA94yKGRInTYK4QxytMQqRVjKlMQd2LxlRK8I65xhytqIM1vnLeRF6/+iNhy9lF9iTsNfuRq6t1tYl+tzwyqv2MwogWdnpFOi4aH9Dk3uStqA9uBZXdC9S4aEzZz1GaCVnVdKSFmtRJgrj7FDf8vO3nEV8i7hC4+2Y4/3C1n7+FU0Ap3v/++8aTrJFR4hH8JSSSBL59sd7nr3P8y03Qls6egn81tP38X/cOvCRcz3JZUIpzLThSfsTQy744Nicj1jzDadKIpQR2XmW/MA925kpHsmJgxPhiw5NeP+DA6e7wHP3J+7vMkurkd3ecdcw0MSGeYZJzAQHPmaEgPfCvAysqGMebVU9NonctyOIJrKb0Ls5TRFyIyypwX3T3EGZo84+u14z+33ER1jKiTUnPNjbQ1UkmM/FRaTxdElpvCM6T9NmtrrIoXFgo5vThEgbTd7vVOhTtggD55j1Soq2Wxq3wnSItC4zGe3n5O4O4zFIDnhx+FbwfY+4Fbo8pQnGOWnU4SSBawwW7CKHszJ4WxzWvGdWhH2qbEpgJAEhkTQgDJYVm825mobEjm9xkiEnNrPVeFwHsCDjFetiq2UtB98zLwONjygJzSOEbVy20PNcjNWSneKyVkGdHd/zkGB7G5dnSN5GVnfw4zXUN5B3LAd3usHyfJcTk8JLrlVefNUaq94RXCGKY+PcBmWwo5VzznaNsTG7f61P7j2qj4IO5BF5YdyifYZV22OtnuOURmDfODDvMzHB6UFYzomxa8BFOjrauFj9K9eg+D2osukiCqmahwTrr9vvw86OCYjCTffPeO+ZOUUGBvWMpd0rRl6Ig3u4WzvnnGEaSya3M0gNV6+NeMa+iBs6RpMAu46vON7wx/cmPnHOMR8Ky15QP+NXnnUZly1praBnax2KrRTWcNviG96t3HjXBuPVCS++qOFHrh3x0rf0uJL5mWfs4zUf3ubFFx9gyU/5rg9ss9Y6ZDbnbS86xihEzmWDNKXk2Bx6vum9Z9AAV8eGP3/RKl/y5xt8ulshz6cslTX69SlPaoX/8oJ1fuRG5ffu3WToA5NoQeZNEp64f8yknbNVhK20xINnt/jy44d56qHCz3xkh+NN4cEp9BR8gNngeNnhwFs3dtjue2bqWZKerWSff6PWxVE6tlRo4jJx2KWPyoEm4brC4CNNHhiNIE8LoZkyj559vXDWew6lGa516ACN1i5E43AM7M6FdYEz2tKUbYKfkIcZI9+y6zaIZRVlIEmgyEDjGjQXkve0KnQexmR2+4QfBWSIFJfoauPHZSPiu8H4sZJgnjIhtuSklOKJpbNjW7T7tbgOsieodSZVbCccSiF3c4JEhjwjaEd37gyun5GkwTUFV1pcN+PsoHxm1vGn79th7SP38MvPbHjOwSXc1kk2eoguUnKiIEyWRoQa3nUBDXHvWfpCj0ekRL1Qwi7UbFwp9LnOgj4TmkxD5D/cchs/fO2ldKHQ5Myawk6XEG/Zski0NmhtrXkRutrPT2IgXvX25RVnfz54EM286QHFZc9S37K7NDVBjQ8PVTl+lurx7+Vt6CKL15LpS++QWFhFmTaJEjy7Ox2di0BP10VmZYfGwxxhuw9M+47fPN3zmg/sMh8KH335CV77ofv5s42e40Pgr77yIvp+g90GShd50bGGV39oh3OtckiUKI57Z5E/eWDKq58QWUot9JmtXvnz+xJfdGiHq1aWeOPduyz7Bk2Bl+yLPP7gKm9+cAcYs+a3+ObLhOvXD+FcQYclZhIoTPiqx21zYmkf2UGbC+/a8Lz7wS3++XUtzz+yhEjPPI94ypvOcGw05akHlnjtNZFnX9LyNW/dJAO7Oy0X70v89A2rhJsTL7/4MAOeIgPdoAzasTXPfN+NM440mXd85TF+6IMb/MX9LYdDz1992UG+8T3nuHe78PYvP8jz3n4PM2nYlAn/6emed5zuecMneo7tX6KEDtQzy1YwnA1wbghMSfTJEYezPOPSdb768Crf+Z4pBycdr3/2FXzXTXdz31ZkdaR1B5B5wcUtb7wj0SzBV6y0vPVcZq2JLIfEEDN37BaiwmjIhDBmUuC+IlDmZB+YOE8zdExNK0yKAcmFkKEvjiKx6pI8vfaWM1QKfTdj2D5JEIeIY44dS8LurMopO3pvAQyilml7VDeYb5/hu/9rw+ufd4AbVhucU2vlE3Das7p0kKymv0n/I7RxVQq6SG8Tv6dEBdvuqSiqnk57XFFaaZk0Qlc8WUaUccdqAqs1uNqmZE9okzXv7R68SA0UDpgqqOByh3NjoPCBU5sE53EtLHlfm2cP3bpdOGH8f5uRTUFZtFiR0Ju0exQ8011hlnbxcYWmzIGG4Kek3uPCQBtHLDUDTYw8b8XxA9cUznaeVhPP3b+Ozjdx+5ReE6OQmWngmskOx9qWD5xVfuSKJWQ8cMnKiL/8iqZW/pXbXrGfT+3OueHP7+cXPnmK91xynP9y+yZ/eE9mfaz86T1n+K7HT2jGFt2JzlFxHFlVXnyi4RNnM9E1rDfKHbtzrmjGXHmFsCwNy23Pvjt7PnDXnHMoP/ahc3xmXvjVG0bcPR9zZneEzoUfvvks/zJexMVrgfef2SZJzzXLKwievzxd+MEnJP7gzi1Wxi1aEpctwXMuisxunLIlDTedbviJ61f5f+46xTACSuDUdODM3FSDG7MRXZ+IYZv7Z4d5zRPWeOcD9/C6Z61zxWSBLa8tbxI/+sGBrV75uacfYVeOsM9Za/3Zx48x64Wr1+AN7VGEGbfOVviBv3mA42sjvvXKJX79trO0pfBVVy2z71THr926xV885zL++K6z/MdzhbFXHnAz/tXVq1x3sOHlf32ag+0YGXZBW2IDVy0JH93NrEukqDfjpDddzyLHpSmCc2JWjb6grWUhSfHWNg5m3sxYkmCDkkVwOEJxFB3REvFxm9+4bZtnPfcI3dYuk6atXc0Ro3YZHz1dn/d+76M5HoEOxDouJt98KCZeVcxB6wuha21bhhgEB+G3Pv4JnnNoP487tMTIt1XZ99Cyj4hHalCOHUH8XoXZiqITVAs7vfCZqeK0pQs9IYEPVRH6/2N4CSQGnDRkOhOHZViPytGVwAsuWuat927zuGUPCEcmggSPC4qWHi9G7JqWCegc32SiG1ifJC47FFj3ExoPG13hAPCrzzzO6SHxm8/dx7hRjsYVXn/HFj910waveNw+vni/43tvPMtyI8gw5geftETxc95xRnjOxY7vv3w/Xz3tKTkyKcrQW7v5wQFK5/nw2Z6vf9sD/OqzDvK6O3refbLj+2+Y8Kef2OHWXvn+xx9mJWa2Y8vy0HNiNGEtCLtD5ief7HnVFZ59E8fNL7mCO2cd68eWec+DcyKe5x9oefO9U87sBLYGx2s+ssM+tjkjHS88eoB/v2qIvqFX/rebTvGD1445shz5P5+7RhbHb33xETIthcRfvmjCW+8d+KkPF370pgfZ548QnOcVf3WK2bzQtA6H6TM8ymaX2NDExuwM00F5/JrjqgMtb7pjm30rDd96fMxvP7BBSiPQs8xEOZV2cH6dG9bhTV92CQ/0c755peUbH7fK4abnZZcs07iWX7pjg4PDEn3JzLvIcvBEl/jSI/t50QnHF18s/NFt8BN/e5rQmMB31Ah9KbU7larZs9AWIXlrC/elJYhQwoArRrEvzhISQxkgQ5RA5zpEx8Bgqtcc+dCm8qY7dnn2RW2dJBwHj+zHNYE+F4L3e3m5j+Z4hEXUKv/Q83ULwGIHVCmzzOCEZQppXvj05jZnu8zXXXkJfhRI4ql04BoGbPzI+vIUcXsSbi/n++via39clS4LM4XgdhnlliE0BE2Ld/aQGsjDGQuptOXRmqhrUloOL2eGHPn3T1/ihpMzrj5kqMOrl42L0VPwTtidZ6al4e7djjfce5bd2Srf//jAW+6c8saTPfuaxKuuNs7DOS38zqfO8tzjBzkxGfjYRub4MeUFR0dc9NxDrATPlSuBn7y+8Ontwi/ffobnHzpAq5kff8YK0w4kz/m15+zn1z/Zo1IYO4fSEKXn33xkoEs963HEn9w15Refe4R/+cFNnrkeeEMfKK7wGx8/x4zMvCink+drHjfG0zHkwBvuhPu3O37iaSs88c2fRtRzcBRYCo7NYeBFF0d++9aerIX1sfC/P+kQ63HG/cN+rl0x/8x4yExGibHz/PxHp9w1z3zN287x0a8a8W3v2uDB7Z73vOQ4L3/7jM35lMY5ljXyL26eccVo4GsuHTHtx+RSmMlAlzyeju20yrtOn+Y/f9kBNkpmnzNLxbddNaFLPd4t8dyjMPiGt9/bc/OZLX7xSZErlpWXnFgjhJ533bnDrVPhGy9r+J6bPf/LicTlqwNBhT4q3jkmvuPZ+5f4macGHrc+4j0nt/medwnv3d5kxY3IKaHR0xezQlRJSFW7epK3RdbU1qB+MCFmNuVvQCsy0gLbTG4/orhEGVqc9syS4Fzhdbed44bDh5EoHD9+HLyFyDsPeSgsrAGP5nhEVPY9F67I+aKlgg8ZzVYF/9S5TeJ8BgEu33+YS8sMxeFKRlTJ0aHJOiYLpiqc1/IXrel3ZcDliNOypz70KTPynmUtzBcqzFxwwXHhBuSRfJha/QyFzDB4diRzdKysNyN+7Y4pTxzB73zpUW49dZYHe8cT1gMbpeMgSp88K41DfM8/vQRecslxnGuAzL991oRfkH1MJdEVZeQ7lrLjD05mXnjRJr/8UXiwV95/ds7XXrrM2d3I+tqMLjVMmsBymxnR8M/eusMw2eWVx1e4Y6vw3u0pLzo+oUkNu1lIBUTmFGkYyjmee2DMb7zgAK9+b89Xv+UMn9jd5HuvO84DGfIw5z4ZIXmglYFX35y59s6Bf3v9iGe+424udZmD7YQDQfl3z9rPdUtjnrze8FV/fZJnHlvh6MTjYkH9nPc9MOfJa4UTk2UOLjk+cHrg3WeMl/LN16zzo08cccc2PPMvNpjnBTg7o64BSfSpIznPN1y8wk8+bQwoP/hBz6uuWaPkOUdGga1B8Zpxbon3npzz5vvXoCiv/cAGp7eVB4OwOzVBmcbTHFTHpWuOl51YYyYDx/cdoiVxzcHAUDxvvL/nuy4/wNaQ+LEnZt5yd8/xlRHkXXJsKFkYXGIkM07nfbzqzz7DqWEFdR2jYcJOMzAZIlIy4pVUjPthJs9sAuAE6ip+wo8q/3cG3lIAizbmOdJknU0RYweliJNCyZGVccH3mbMy5q5OePZlRwkuIKVHpSFny4D5h6AlPCIdiIo+FKosprDTzrGR58zLlKtWV1g/vI/fvf0krSRmalxMFQ8+mz/ggszPz3YS1t+GuljhPL6qQgxuPJHCVfsDH9t0pFJwYWH9x8TizQAAIABJREFUrgaoR/hpWpHYIapEn9DScO1aIEjmnXft8HsZvmSt4wefss5v3r7Niy5a4hnrEz6+0xGdI9HxSx/c5SO78Pg15ZPnhF942iq/cfuUO7fPsj72PGEp8PGziW0JfMvFkUvWJ+w/k5jJwINz4eNbHW+4c5snHB7z4oMdr/noJisFgsu88pqGlRD5sotG3Nv1XH1fy2f6xEFvMCXJthJ5hJU44ZPzEd/9jg1+5CkH+LEPb3KxjhgHC80aj8cszWFox+x2wh98ycX8xm1naccN/+LKFYrzfOXRMc4X+gR/dv8ur7+9555e+Lbllo+eS0w1QOf5i7vm/M4Dm7zmiUd5zkXKP3vbWUQTnW9506dnHJ+0PG1fzz89uMQPPckk7z9/3SpdsRzX1z3tIB2Zn/rQnM2bNvmFpx7gvSfP8Zw/6/lfLxG+/dp9fOu7NvjSYyN+8/Ztgu9YkcRXv0uZSOFfP2udt9y9y+AiI4HVpuWll475hQ/P+I+fnNJ18PVvvZ//8Lx9/LsPnuJlLznBD18zZn8rnFgdcevGnBccW+Vj5zIhjvip6xquP9hw3I959pHA++6Z8dLLLqGUnXovRj617XjP6WS+Is0EV42UVTpffMZ7E5VlCUizjopQWLN71FlcSHUNGRbUUet+MBQhlEobExgLvGtY4gWMyAqewpAKzj36ArLFeERKVKrTcW9UH0vjA2su0nphXpSiA754Ci2JjlgGkFSJ0m5PiariHuK+NDNdFalRuRaixv4oQtCCOM/XXjLh32xM0Ub+X+7OO1yzqr73n1X23u/7nj4NBmYA6QoiitLsaK5RY1CjRq+xJdGQxAQLYkksSDTRoKgxXiUiUVEQMUZFAY0dbIhKEwJTYJAZ5sycOe1te++11u/+sdb7njMD5soA95q7nofnMOe8ZZe11/qVb6FfQTAqmTgPD4xYY1niY8SxnJ+xe9gXfXE9YGlpx7z0eMb6ffhVX/Efd5WMtxRPPGqKTMF5t/aYtJa/PTbnRd/uMt4Akzf4+BPG+OsftnnLIyd49Ffm+OVcyf88qODF3684Pg+cfVzOf3YD2++eQen9uXG2z0ThuaETWN/MeMZ+4zxn/zG8KjHk3PB7K7j6zoLnXX0X4w3NqIGJwtINFUesymm1HZu7joMlh8JTk5EpzRVPnmL9WE4r0+yshc88diWFsYzoml88Yx1ooefg+tkOL71K4aWioXJW6sBq6/jghg6fubXNz09dw0+mOzz/4Cnu7tRcOq147/XTvOiQMVw70JwsedXD13DhxpJxHTiymfPVp+9D35e846fz3NIXNnZ6HD85zldmt7FwU5NLHhv4q5tnkbLg609ZwUdu28XWusuv6oLrdmkqpzG6wRMnhNMePsZLvr+Vjb2c8w625K2cc3/qKZXmx7/q8Ji1k9QCa1o5ix7wfepyFF977q4qfjDbpecMpmjRFsPPFiqe8PVf4WqNVSUtnVG5mirXLNZdWloxX2p0aNEs+kxmmhP2awDChk6LIH1aSjOSF1y9vUeeW8Rl0SKUWJ8QiQXSMPBYTlyaoCLxLasiYld5mwicSbIiKESbiHFRUam/NAHjFaI1t+/q0VfRlUAbG10H1b1vmL+VOJCBgvQSFZchVbwnDus9Yj3WewiBWnnqcg6djSBOhgL1pG6LIipjDS5AYEkXxA/IWiLRCyZ+DcEY+iI8dd0o775B2NlfpLAZmVKYIITE/JTEioyVe4jOeS62yWBIsxciQ1NUYnaio2N6DiPtFk9Zn/P5DYvokZwTVjZ4ylrD+bf22Fkr/mNbzacOneJVx7b58i2W56+3EHK+cKfnb47VTDYC/7ypz0ljNX92RIMXHtbgMzfnvOghip/umOC8WxYAzbqmoZl1OciO8fkti5x98zzPWTvGE1YLr7t6ljDhaeSaf75pjq44DihW890dHT6xaZ6qdJy83yS90GckNMiA3Ja89aaSVmuUsdKyYqykgeZVh4+yudfnkrsM+6uaTGVs6imU7dCyK2naip2u5s3Xlew/WqLNJLe2az54/Gp+NuP555v66FK4QSlelufoRk1ztoUNATKoDFSS8727ZzA+Z1aE8dqykhGaTUWrdhyaF8w6zc13O/bJAcnY2K7Y3ssJ2jHic7QtObABH3r8FJvmHM9atw+jtmJrx/LSA3MuuKlkSxnb6bfOLPL+Gwz7F4p5XzKSGzK1g/M2jXLLrKJVBzohoGybKWMYUwXnHreGY8Yyepml6RWtPCo/HfjFOXA93nNLj562HD1uOPO6BX5/bYNXHzHFa3+wjdI4XGXpW8cUlqpWKN3HJGN2I7GdPWQRJ32QwbNjg0I1FC4ETBbdEM2yyNmg8CFgjMI5F2siVJHGYCucEpriE7w+LNUP94Ap/HbiQIagqIQHkQHVOkTlKWMxQVj0kdbeDoqgR9A4grWo4CPHQg++Wg9BZBC1Nbxy+FRIjTbTNiJWvVBoQ0lNZmHCwgmTNf82PYpUczTyJt46hCzeBAlkKmNgcTBUgVcRzoxEmcMwtOqETBS1ijdwUmtOe9QUq3XFhZt7HJ5n/MOxY9zR7fFPN9esthXX7TL8Yq7m7Ufsy207pnnFwU2+ubOHkza5HuX392lx9VyPDXXBaw9tMa4L3rHxdn5yyn5MjNacf9w+PHQs0MwtP5sf4aItC6xqNjh5aoLDxypWmRaHrdFUrsUWvcjh42OsaHVZP254WjbGI1ZoxnLDX/x4O91pzcPGegQZp1MLt8wbpjvbmCharGsUvPMYzXhWMZkVPHGy4pz/7HLtTmGs9vSVouP66MyxKiv42GPWcsGWO3j4mOG4iSaVd7z62lk+edJq3rdxF1f8qoPPehRNy0OnMg4eyzjloIxut8fmnuLdP2uzo7aM5ZYxK9R2FluvZjt9XniY5rI7dtFz4Fs5qAWCsvRknlEZp0OFVYYZEbbPe7qS88gVHuctW3s91nnLH65r8IGNsxzcavLhx49T9gM9VWFDQd8JNatoV31efdQIZ13rMLnClRUqGB52wCQd1+bLc5YP/HSedj3CeBb42ElNVtmaXbrEB4sNQiNT7HI139spfOSENZz28IK3XQdTeR1V7W0DoypERfFtjYrmUmQMKnqxkzgo6KtkBaOTMFZKm4fBghpubs4HDBmhrlBakS/2WLN2Ao1DmYIo1pwRt0P136MGEmJykaDsUVpQyZKUWwieoAKjCD0f+N39piisEHzARbwZYix1krQPIf4crJ5ey5KGZ1jy3g3EFbYST8NY+lVg1GhOP3qKH31vmgU1grXgyFA+2iUYbJTiI+mnsrQbRH5F0gqR6FEjKbyMd9Wwq4T33zrNT3c2mC8Dfeny1a0Zn9rUZd4JRVaga8errrqb1xw2xW0LFU/+5jxTWkAyPnpLxVMPMjxFxtEY7uyW/MstXXTV5NHf6OArxwWbe9w1V7Otrpgr4Xf3b7A6V5y02iPBckun5tQDxtDBcfaNcMYjGvR7I/x4Z4+dfcecs0yXPRpeuPiUSdaM5jjn6HvFs/Zr8rT1E6zJHA8Zb/L9HSWvuHqRu+Y9Tzsw49xHjtPQhndf3+GXc47DJpu8yBh+PGu4283z2ccdwM1zfV53TYc3HC2ceUTOAVM1m7YHqlCQu4JG0Lz56IJP39bj3Uc3qUKDFRm8/IgGTS9sK2uqUPDHh42zvVtx8fEHcM32Pj+eCTxm3xYrRPP5Wy3r8sDaxmoq3+HDJ+/DNbtqXndEi3fc2GW6LumVFm0CAcuRd9bcUVW060nsKBw53uTPrt/BQq3BLNDCgIH9mgXnPFr4x1/AvhL41ONXsn5EeNE+QtcbHjamefkRUwQU+5rA2skWi9UuxDbwpSPXCl/BEyYNT9l/kud//24+fOIUR492ec8vK25bKPHiiHZGAaPijLKi93BYvLd0OVm6spRqDJi6SqkhKMxLZO1qA71W4LErLQ0MXjRReHVA+XCIund/3QdzKNmLOGdQPxWiktTGOzZx5ZVXsu7AQ1G1p1uVhKCpsw6NbsVi19FVNXXtGVEtKlszXhRMTEywdv06piYmWbFqNRNjoyilKYOPSuUQq/xBkXsdRYiVpk+XcduiDAs0QpPrF9r8yVU1M34RrXKM04j2ERFLjlAjyYdGY3BSolXEmRiTEYJLRPIoQViLS2S3El016BEp9sEEgspwVc1UbtnlOkxmI3jRzImj5YW+13hVojLLSNBkAqUF46OKWZAKRUGlNLmr8EXA+gwTNA4Y0QVjzS47+9AHCi04rwiqgQ9tRnSLli3pqoKy7KHDKCGvUU5x3ITFZnDDrKOSjBX5Ll55yDo2LC5y+U5HVVXMdA0NoxlvwFzped5BGTsXcq7tdNl/LOPOucC2OrC+VbGGBpvqmpbJWKk07zluhKtn4KN3zLLYF/7pkSN0XYMnrXW84IcLuDacetAED1+TM2k0/eD4xc6KHb2Yfn1z2vGGh7Y4abVhn6LAZp5MLFCB0pRo/vIHc/zB+prX/rjPSw9r8dxDVnBIo4FTnqaJTGZMxXfv8rz4B10eMwFf/J2cbd0RKtelkoxKakrfALo8amWDU745w+1t4ZR9LD+ZNuzq9/jYySs4aNTx813jzLs2OhdOXTXOE7+1ja7vY+sx3nNixgsPGEHI+cVMj9//9nYeOjLCe09QHDs1yntuaHPBxipigHzyR0oWJTpEOIKWyJwl2VySVOqiH5Ij+gu74SKilCFIiaGglEjGq7OSvG9oTQS+/fg12EJhfY43grhIypSwu2ZsZjVlcGTe8M1vXcGJx5/AihUr7t9qcS9jL821B4bNxHBMhYSMKwnNnGap6JpFxpxmWy1kpuC7d97Oj+7s8MZj15HrsWSXECJEPbFfJaosx4spUXtBgsf4pPMA1N5TmIxuFQh5hqrhoVNTfOZ/VLz2Ox1+1AnUZY8Mi2QBGwK18ejgEHKUKiOiVbtEodd4HxcSH8p4TCHiUpwqKcjRxtENGqdrgq8xWrOlFxg3lpnSM0ZJrRVdCTixFMpRl4ZuXuGrBrmUUfpF5+AK+sExbjXTDlaKZcY7xrKcvuvTyWp2dYTSS/RlkcgbUrqHM4pOqFgoa6xE46JCeaQscUZxzUKIxLPC4nsO5zPeed0cKhMkGLQYigxqU9EOTYyUfHlLE0ePhg5sXcipabM2F+rOKFtyT4smZeXYVnhe8qN2lO0jsEIV/N0tFUYC/3AjNIqMjoYL72wjWyuoosqIzRQjIdAzNUjGm3+2gywYFA2yoqbwlpFc06kdE0azKwS+uTMwlhk+eGuHz26scJJRWE9mhIlmhpcKVRf4rE2nnuLqHYb33jRDxwH0MKGk0JY1LcM/HDtJFTQtI1y1HVBdmirDZJpOMNzem0d0zXg3J8uFCVuRa0NHtanrFfxwuuZ9v9zJ9TOOwsAdTvG8qzq8+hD49vYOxuQEiboehlQjlAF506c6HCzZN4ao3hcgJtKDpkIcktTvQpr/s/2auRIOaBheuo8iKxqIeGo0kiQrfcIe/L+wDt27CEQSFybVFjbcsZmvX3EFhxzyEFxV82+33sWRE6Ps3xKu/tUcl918J/utmuT31o6zT66pcstIljG+YiXr1q5jfOUEK1fGCASgkqRAFQTJVRQBWrbCxsjBkBtPpQxWBPEeZxRX3Nnm81v6zLU926pAFSA3jpBSJ+dcpIZLhg8BaxsQhFrqFJ8YSlWhQwqZa0tuhb4CKWusApMbqiDgPNYUZAT6oYoSgjanoaGqY04alCfXkTdRBUWuBQluyctUGTKTU3sHRMEg0ZGwZkOkhgcPIYs8EBViZDKhLWWIhkUiCpWFiERUmswHShP1R4wx1HUdGc1kBKmj4RMZQVXgGhhdUYcGuSlxQUGoEWUxmUN8jjKgfJT70TouuKINxseOj9FxB9UOfBb1aWuJc8Mrh/UFKvOE4GOESaBAJdsJEzVmJEo51F6w2mO1pap7NHSLuo5i3ZkSKolo1K6LEUlNhhZHGTRFVuGcxeiA0ekhzCpC2aKqu0gWtUg6EpgyGW0HudFo7ciTWv5C5ckygw1Rqc47izMeqw2UNT6z1K4is60oG6CyZKi9XKD7v36kIodsoDYfI4+gdKrTRZKcJqbUHWfJVc3Dxg1feepaupWO+qmyBFv3OtYfl3ddfqsjkCEXhhiFGJ+iAyt0+wKZ4fP/eRcvPHolR66e4LPXb+YPVzXYMLvIxixw4topSEI3XgfuKaekk6hQtEJCliwfayJNX4tQJwEWb4TMKHICz13f5LkHRBhwFLu0qUCacs4haC2R+UKRrDHjwlTbiqpSGAksEp3UnfMEaymDJgQoXUkQQ48aUxXUoabOIfgcV1b0tIsLnvJULseZgCstoYaeDpTiIvFKSkpfILqkX2rEKPqlwamKqlR0jcP4PCqo1xZvPXUleAlU2lDVyROYQBm6hCyjrARyg3YeUJha41RarLTDe0PXeJBosN1UJZWCSnWph4hgE0PsfkahYpgpyXFOB6EjiqZ2oBQOTzLNoK0DJngwDucNVgWsNzgd0D4qlAcFuVeQ57RDmxHdBBIUn5j/awXOG4wZoQoOU2SUVDifobRhvu7TyjJ64gk60IwqPrhaETKHuIgX6gdLLgU+eHRuKXHkJqPpBE9Obtr0pUGoLFnSlipsQV33KHWGUZqRosDVmtr1CPkoma9wpqDpNbXxlD6L2IZE/AzeJ9FoEJPU9PbspmqFCrEGN/yVJnYjB+LNyXS+pRx1sLz/0S0UFZLl8XwkR/BolaF95ND8n23JHvixV6LKS3T+9LvUlSlqhTNw8qqME1cdRiNfYGG6wKmSDfOB9YXh8DVrMNZEMRStUrV6qQWllFoyg04j6OjeJpCk92LTqxbIdbT9cyogOhCSG30ujRjia0MIUIvBSEj4Exe7SWIZmkhLslSQnCwPgKEhkfiksggXRvuUu5pUjC2SEFIDoU7t7RwJLQaFM0WWUIKxrack6RsLBEYxXgg04onqwS4SQPIoTCEpT4wviFV+cYgWAlEnNWIMBncjyiPUhqgF6gLBm2ijIIpaHDUBKk1fe6TWlEojdY0no68rQu2oKeiGHkYynO9TqXEq8SgHXQ8ERUeiEHLfGySUEUthM1yw+FJTSk0JOOdxXlHZgtITOSBOqFqjEMCLpacC4iwNKlyo8SFEkWk9ggRPQUElFd47gjb0SktfShq6xY6ywhhHrQXVd7EYSYbXVVJXU6g6R4tnMdliVCziS4+xDuUDSsV7awuHrsEoT1U7FlyXhk0xU9mhUp5KZ/TpIcrScIFgQow2g48FVB2SVq+LXUUylNSI0eggBBMwEpIAtkWZEA3QEmCyJtDQmnbd5qjxFXz05DHWj2tqcpQoAibq4sT8BWXAJu8dpeP5OSAXQWkFIRLtHoyxFxFISOS2pRRGVHwwauXJcsOOdoaiQ7uqGadPbiyHrupxQGMVjaDA1eRZkSKPJVNgo2JgF0WWB9ogMpTKj98eSVUohfZRdVwkgLYEnx5sNF3tUJIjIUoFRM5NMneWeNoRuTqohMeuko82STE8lKSYBuCTpUVSpQ8+5rhGq0RiWn4p1W6fGxWX8vQxgvGKWoEKQqVUcsEjlZZUTFUUiMt3C4mjwE28baIi5TsKQZAKdCQrhUgDyAkEGxdAkVixb/qAER2VxtEwEqXGRVtCiGbmXjeiKbbkURldRlDeReSkJUruBQEdAX+5c4gZiUumcqmFnlrpqoyL6NArJt5nj0Yph/EWb0CkTuxjjVNCITmu8gQT26EOB2Jw1CghppCYyISlxscuP6WqUD7HSaT6KzF08SlFnMQFqGuh8hHI5V1GZYS676mVopKa2kElhsrHWt8icR7U3tETFRc4Z6lCjcPgasFTUzlNaSx1FTEaPZUhovBOUxuNk4DzgqsALQTJCbbC9RXGWjpUOFcjIhwyrvjrR+7Lsw6cwoQuARPJnb6Jlqj3SggEY2PEogQsKFG4oKIQlIFQBtBRsHoghr78Wd6TzHpfx14sIFG6f/lh6KSEZLVi5wJcuf1OTD/nT49Zw+1zNaPSYKE7zvtvuoPXH38guq/iySYIfEhArkGrVvkEWh+I0O727ZHvkkkU4Y3doPgZWgRjBY8nCxlOakJi9cac0i8tDizln1Gk2SOisWS79dNlWSwUvEIpjfcxl4+KZWG36Cm+JwkCc08wjxkslihEDbTNl45HyUCBXhF0xKcov7uLmkhsdRNCEuRVcQdTKi2CUa1MGFhtRmEmgiZoQ51qWBD1UGsdGdaZKmJ6F6KiVhCPDgalPMHEhVMFqMVjsUiI17O0ICEWEZ1I9M2RqBYuwYJXSJac6PH0QzQRr1W0YBCp0c4QjEaFmL72EMSmvzuFFkNARysHE609hKSDoXKMUsnewEYPHgrwHmMVDKQkVI0NWawZSHx/hsUryELSNtXjeHHDQqhROml06GgajiH4Ktp1aEFL9CfySiU+y8AHJuqmagBlU6MAIDr5ZUKavTFiDInZ3nY1EJjIcuoQI1Blm/GeR/OSwcej0rf5gWyAxAUi1wEvFoJnQdVcetccj9Mm8XBY9kDd/6hkL9i4yVRn8N0JRitKYcloZougDOsmLTsWHVfeto0zTj6coGq+fadnMlimrdAaOhEkT5hl0oYKg9cOfMytvWLoDzNgHIbUL4+FQ0XmBIdQ+vjQxAghQoKjkVNARBNIN3mZdeHyB94te1AHYs7D41LRFMpow8DNLKqsy/A1CQkAyDBaWZ6QDRackN6vwnIuUPxuJXERVFGFGSEpcxsZLiSSLCsjBsAz8OoRFYamTYNz8hIJWgqPUYIhJ6hYWHYJFRzP3UVDLZ0qXMrGiEiIvjpSY5XFhkDQNd4rGgJIQOsarbJor1h7ssxRe0XTGmpFVE9XdXSSp8DjaEUABZCBDcn+0hO3pITklABZko8gPlzxrvg0+VxMQ0ORXsfSaxMTNt7EGMFg3eBOpBvrIlJ5+CzVZGo57SFuTyghbnEuWissWxBQYFIreoky4Yb3BOr03vjvLL3NoOMqpX3q0gRW5B4kbtINrcArughaIt9mgKH2xEhbLd/ARBNUhSLHKKil4pzbZvnuTBSmFkibiR6e2f0Fu993JGrQBB0dt5bXQAxRxi2oJqOuYEbgim1z8SGzFf1uNE/qKY2EKu2iMiw4DSa7Birl0bVGlIcU8uu0M+vBpBocj45RQK1Bi4o7Sog7oFHRYmGgrQrpoVi2YEBMXQZRgJUBbA2Wt8UG0YXxse8OA6TsHtcHEydkAtbt+YrlLTudWtZeLf+uAQeIVP9Ytjh5UosvTZrkyL4b5UGWllqdiqeKaDY1SDdrnSKL+IZ0rOnbRYFEVmkYLIwKpI6AvHhHLPgYJdUSwFtEaRyODEuthdobggh9Fz+nhqiVQUx1Y90qpp8isbsjmJTjD6QfB9fLpkhzyUhKEz8/1uOSF0uw8f3KpUV8cEUHC2vSJk2RwiBqHAp6p9eGVBiWtJjJwPN2eFuWNh81XBiWIo8gDqOiBEEK7hh42oioeKwkW0sVgYyDkeks1ppEE1AgAeUNPnjwCqMyPDHVFOWwyiCqIriMYmAroj0WxyXTC9ywS5jMasRXw87PAIah0nnsGeXfl3HfYxgdhi1tIaLtBuGgl3hCtRKu2bHASZOj2Dxjbr5GkjitNTWamFaICgQV3zuAsw+XBj3Y0TVio79q/HvYLS2IdYw9wnshKqMx4Auo4a4qsqQ9svQZyyIEwr1ezKDif6IGD5weRk2wtMDED/z1l3W34nCKKHfv38tu6dCeQ6WUZemn2uP7Bj6zg4Vq2bWKKzXK6+Hna7VswVJh2CkJEnc5lGcgXSl7HP+gixA9d+Oi2ZcI/Bs01vwykZsQQnQXlHhsdeq0kVIwEXDLrqkkaT8RwUsE+wUhYnQGLX2R+B0SuxohLYgD31pJf4/HJHHBW369RHDpcwbfBxEBGiS+R4JaOhYS7oLB76LWR3y/j/VBTDwmlXgqSsXPCiwtyhLSQiRJ5S+ed+mXze97pMGR+zKgkmgNKAhiMLngQk0ZFLvqLn93W5eP3baINvHzGtErdun+qUFEcv+ikL3owsTwMU7RBCZLj32VgXaBrdUibzz6IMqsYsT3Oai1Dwu6y/MPWYcLQstorDaIMpgQlnaDlAoBKbdP3yW7l372fLiW/3s3DdRhYysCd+TXvP8e53gvzuyDaEEYbBqJsZC+b2+IS78O+HPvx5ce4WXp0q8b9xb57P73pfeH5Z+TirkqCAyjojhLhVi4W0rZBFF6uAOZSHvcLdWMH6l28/0ZCDYNko2QAFAqLdAqRI3dWENZ+hwtZlngHY9xcJdFxwddB51qCfe8dMMNhiXdDJFUu2DJYDt2x9TQO/feCGqDKEW0ip5AEn/KoBqYztUFhdYRv4OJnCsVJG1Rkvx4BgteYNhQCAMz8KXvX6qvpfurPC5oMh2tO7w3oAvaZc3zr9mBV4ZJlVGGikxpSpHYwdz9stzvsRdFVEkFOBiyDVOuF3oeFTRvOfbhlFUXUaP87uEHMhtqslpx8IjFYSA4kgnjUvtR9HDx0Ok5WT4Rlz8Syx/aPR+2e3YsWPYpv+kp7hlBxG9fqnUs/919+eDfbNz7AnLvUcVur0i6EJp7LoC/8XfHA9h9UU4Jnl62Ew6Nslma0Ejykh2+Maa2QlT3Gsi1LL1vdw1PHbfvmBwPfjJYuJded48NZDgxlpty7zZjovdNKl4zjCYDFjusSw0MxwfGUDHKYFij0sJudavl568GVyqdZFi2sw9eKwJYg/FhuEAACVSYQGg6csC0qGhi43Ts9KX5bkz0bM5UjlZgRUDrqAOjKz6yaQbjFTZrMUeHpuRkQSUw4wM/9mqWxTBxWQqTbmgt4JTHGI0ho7CBUWPIa4U3ih6DCZPsFobpBrsxcoffs2yyDaeuRENrWLpBXvndOiqwVNxcLjS7p1r7sAi7x1i+W6qUpy9XeF+eQiyvUey6uF5fAAAgAElEQVT5/uGuIffcyQbHsdvuIrJbV2e3BVIGE+6/iCxCSuX2cvGA3a/znr9bftz3fGPaRPaMaJZFOqJ2/6Rfp2Gx/NoBu90jn+wsBv8ppbBWD1/jUk1FJAx/F0KgruuYNqc5oSUali0/hqW5s/x3pN8t/dzz/JembrrvOkQb04TTialUrIdoAZUpGlbhXIeyLOm0F6jKDpaoeRMjFoXyZjexoMFcVi6aSClpY5SiFjBGc+ldXa5eqGlkHqMDYyJYbfAanLj7NS9+3dhLSUNZurIsTQTRTYJ0UaHC5Iqur9EIwXh0iLoHdaixJo8TILUXlxYJP0w7gmIJxbd8nqmAUZYgEq00kTgRBjWUELDWEMRDuOdD61M6Q2rpDnYXw8CbN3Y8HA6rLd5HEE5ILdOoTwJL9Oz4i0G3ZWB5MZjcfhDWDxaRdI4ewQ7ToqSSoiMLeHBNPbE17lwqxt1LdP7/26hD9OF1saS6FEeomOoOTcNShyouJJBlUaQYFdBqCZzovY+CwzrKRng3iOEcWpt7LPL3d0SJC5PiFxPBXelYghe8qlmc3kVdlnhfR46Sih3EWT3D6Ogoo5NjqfWtUHVqHkhyKrCCr6GwHkWDPhXdWvG2W7dx3VyfTBdoD6LL2PZ2EVj3YPFk9j4C2e1DUt3ClWiBWkc+hg6glIlYgBB791ZZgji0stEPdPnnDYqTWi07YTMMjSHelHrZLgOR76H1UmHQ+5A6kzEcjPBolXYe0uIR184B8nXp21SyhLRD7sdgci3rO+0+2ZZFIlplaRFZioT88KVqWO/RstSNYVnb1aCog4+T2weMjouiTguL2gMX/X9Dder/5jCkesayyHN5oVhrHc2ekul1lpl0j+Lrcp1jjEppQeRMKZ3uvYqv9cSO0q9Lf+/P0CrOm6XWPLGYKoJoxdyOWebmd1KXLnoTh1ggVt5R1jUzs7Ns/dW2SF8gaspm6GG3KnjN3MxdbN16NyWORhDeedvdXHVXiZMGpevhtKZQBblKHrrKs1yqb8+I/H6d7wPxtjB8oOLkz7WgioxeneN8lzsXegStCCEZEhMNqEWncJKlUL4mKlbHIcQi6tLJ1lKT61Q4047MKnQQjIlw840bbmHrXXfESWiifqpSejgRtdYYHdGNOpWxhzmsyLCQN1hYBu8Z5KfK7r5rYeLi5okTxCuH0dkwfBatk5lQXGTi5NJok26ikWE6pDON8xWogK8WsU1Dr4LRRobSscbhze6+Ff8vpPwfzDF4+DDxvg1S2+WpqNaazEArT9tA3efan/yAhlVgwKgYWueZpjBgNWQmPUMW8rTAiBlWU+81xdybMQQJStr1JeGarMKXXXplm9w04uKnszT3MlAZVuuoKVLXuM4ihYBVsSNkjEErT93bBaFHQ2r8/Cyf39Hlhl3CaKtBHsoItAueKvhI0MMQjELc0rzZM92/P+M+LyC/7ksHlflcZYQ6Epqu2LiJnf2MnWU78g0kcgFQEV4L8QGQAZ1fhAwVCXbx24AMqyWFhGBNMWRkZlrhPZTeUbrAzuntvPC5z6PsdVDEfNnqaFEJCufiYuQkDLkBOh0HSmGsid15k8yhE1o26JhPG0n56bLroIPgvCfTBoPChhhhDUJtk1KRwXsybVLAolM6tQyPEsBqQwa84uWv5LJ//wIjGXT6ZURFeMGGFDmlSESjI2L1/5ORpQjNShSnsim6HIwI1QpUTtg+s53zP34+z33B83nLW97Cx8//OFd++TKu/fk1uLpGcIhApz3PVy/7CpVzKBdw1DFiCRLvf9r8HogHKuJ64nyOMJ04r20I7JqbjYZRwcV5KQIWHBUKF5HJJiOowOJiB6cEJQalaxDP/Pw87cWSqhZmKvi7X9zOR2/YwYTReF2jlEkRdEg0ApZqYvrXmcnfv7H3eiDLuH/LaxhaWyptyayws1uxeX4nN+zyPHofi3MV2tgI7gpqt/B0kMYMQlFSs0xIMoSDuniIBKH4EAohOPqdWRbnu3z6M5/i5Mc9nv0PPjimAjpGHl6BFZd2sQRrlxABSVqjggOtCeIRyVEhQuh9XaNM/DeKIQAphDCMSnwKk51zw4k+bEvr2LFS3qN0fG00B5foZ2qESDZWhDo+LDVCCJ6HHHwgL3jOH7Jpy8nsv3Yf6jTZvfax5qMkomIxuy1C/+2HJi742iDB41ysc2gTUFoniJnm29/5D179mtfSnVtgZGKEVqvBhz/8YdrtNhgodIOTnnAiB+53AF+9/DJmZ+f53nGP4paNmzj5cY+lX9fkxUAG8L9ue9+XEbtg6acKiIoRq6sq6l4/ghuVJrOaIs9ZPbUKp8FVJbMzM3jv8QFcVYMPGGWovMJaRWt0hO7OHbR1wenXbmGHChxUtOlmGbahwEfMk2LQLdIpwlUQ3L0e66Clv7eL532eeRGvscfvBpu4yVDiyVT0sWg1msx2MlS3G1EvYnEhRh9eOywmPpQpX4wnEdAhQsGDUrEIqxjWQAZF0zoElM6xVnP2We/mG9+4nNHWCGed/Q5cDTo3ZAqqOuaXtRO6nUWUFvquS9Aaay1WDNbkEELUz9Aam8XFJM9zrI07k7HRXV0rSdoUMZTOE9RQa00QF5m3eIzWIAbnq0hwUjXGKkymaVhDsIFcGXQGuYa8MMM2nRLNG974Rg49/DB+ctVVXHLpv8VrY6NLPKSUJ+gHNBz9bRgxlbMoBUVmyaziZz/5MfMzuzASmJneyuvf+Bpee/prmNlxFybPmJ6epr3Yp9/vMzU1xtjoFEWR8dNrfsbnP3cJrvI0GyO86+//jv/1Tx8CNEXDRitUA3YZyPABOQcMwZBqd4MuUAmA1jbCysWzYtVKlM3J8px8pMnkihUEFch1dMutXNyc89yiBIosZ3T1FJdsWMRRsrrMWfSOzGoaVWToDtIxLfHhtn7QodKwB0bmgZg7exeBKMUwp1j+YaqMQjVB0/GB0NSsCBVrDm1y2YZNPHXdOpqYSOQM0RNUJxiykSVosSSegwphALrYrT046KsH5/Fln2uu/RGnnPIkjnr4ar531VU84/eeSR0gM5rRQlN7sMbyrD/8I/71gk8xtmJyyGP45Ccv4Fvf+Dq2kdNoNKj6NQ855ADe/tZ38IubrmN2xzxKSxLmKSmyBocffSQTrUluuOFn3HDtz9m6fZqjj30Yz3zGszBEaQGCoHXFv3/uC9y48Vbe+qa3kGcKL/Cdb32dubkFTjjhBLqdfiR8AeOTK5iYmkQXhlEZ5YfX/Igdd07zuCc9jhe+8A9xPqZ4cUeJ9ZMYwciwIP3ffYQQCN6h85yqLpGQcd6/fJROZ5Esy9i6dStzc3P0q5LRiUlylfE/nvoUZudmmJmZJkjcbZsjLeqyjxobIcsyut0u37/qB4yOjrN1y2b2X39gIkUqlrLYB+Ya+lTHk0whSejHVx5joreRwdAaa6JV9MQJKILSZEUjRbtVFFV2jlBojMTIU8qa/5gJ/LDTpqnH6WXztKoRgo8bprjIUh9gUURqlGqgCOgHKc3duwVkqGweV7XhA60Nuq4RrdjVWeR3167AdIXJInBwcyVaQylRWR0V0KIJxNxt0LkwSlENW19RP3K5Pq33Hltk+MphC8sdd2zndWecycjYKGef9U4++M8fIShNbqB2fd7zrnO44abrQStu37SZP/6Tl6OM4bCHHMx7zjmHDbfdxv4HrucFz3s+IQR6vR7nvP99eGDjrRu57rrrmNm+g4svuYiXvOQlaFNw2trTWDWxknP/8Rye99w/4Kyz38kBBxzEpZ+7hH6/4q1vfSunnnoqx594IhPNEbZO383pf/VXXHPNNXzi4+fzvg+cy+rx1bTbC2y69XZEBVzwvOCF/5PmeAvjDT7UvP1tb+MxxzyGRzz8GHzlyI3GeY+xEUKthbix+N2BVr/NY/CQLse7DFI/ESHPNDUZCvj2t77LF7/wBX554000Wk1cHWg0c0ZbY6yYWsNjH/toDlh/MLPzu3ji45/EJz55Ho9/3Cl41+Pb3/4O27ZuR6mMdncxppVEDMlf/vVf8ba/eRvHPOoR+CpQFJZKPFoZfHAMeEx7cqZ+o/NjCSviko+vQCyOp3MUEYp8BIcjT+caQXSWojlCv9dHGUtdLmJH1tALnkpq3r1hllvaYGyB9PtkWJT4xOxOj2WIkyJC5bPU+49CWFGG9IFdSO47lF2F1LZVDPRABoQccTH0aihh/2bBNbva3LZ9mk675hHr9uWIFtRKQRgsOGpouDOYTHXq/w9OM1blE/4CKIqMuvY0CovzJa87/TV8+Utf5I677ubiSz+PthmtRhHzQCec8aYz+Ph5H+Okxz6Ohx35ULRVnHnGG/nr15+OJmJaPnvRxfz4xz9mtDVCt9um9AFN4OnPfjZPf/rT2bDpdq74+uWc/Z5/JCtyfNVjw8YNXHzRRaxesy9/9spXcfa7/o6vXn4Zxhg6ZcUrXvEK5ufnqbp9Dj74YE4++WQOWLeesbEx6p7HrrFomzO1eopms4lSii23b+Lwow6jdp5Ga4QPvv+DfP3K/+Ciiy7iwk9fwGFHHMmxjz4Oq5soH0vP0YkktY4eBKDQAz1i4Xj3aHJQUxKJWrBaYGZuB2864w3ccsstFLllZHyEE084nm13beWZz30Wv/PkJ3Hzf97Fls1beOs7/pbNmzfxkf91AR/80Pu4+MJ/5QXPfwnPfMaBPOYxx3HmmW/hhht+jslyRHdwW2tOO+00PvPpT7H+kMPwwUSuzLLcPKYz9/16uoQjiqz1BP0fdO2UGhIAjVUYo2IEmchxQYG1GhcMDRUlJCsg0z3ecdM8397haBmFUTm1rihC1NmTUBFSW3vwLAUVdmOKB8UDvnjAXlyhiPYfAKjShUnMBo2grKHtKyqbcetMB+0aeKNQ3qFEY2pwqgYdtU69SliRdKJG6WHXYgAHHnQ+RIS69qhc0ydWmq+55hpc8Hzkwx/gtFf9acwZgco5RhpNrLXs2jXHpz/1GWyjYH6xx3nnnYc1DTzwqr84jW9/91s8+9nPZs1+a3nve9/Lhz8U82SrNM1GE4NiauUqWkWBDpDlLc56+zsQBSsnJ3jVaX+G1poTTzyRM888k9WT45xyyimMjI3xxKc8Ga01K1eu5G/+9m855NBDES3keU5VVcztmuWc9/4jmzdvZsOGWzFkZJkhVB7RmqnJScYnJ/j4Jy7g8iuvJLhoRRH8ErsTuEdd6rd1GIlo5UHrOzM6tWwVRW7ItWL79J28+CUvYbHssP/+a3nWs5/GMcccTmsk56BD1tPrzPOmM8/isMMfwhvOeB07tt/N+977bi7/2iX8/dln8bSn/y6vP+NMHvvkx/ODq69l27aNjE6uZNWKKbRX2KzAasPbz3onraIg0xG0ZSS19Ae+rSqkKPg3T20MScdFYm0sEFvGXkX9FotQ+gqTivFAdKHzMSXNjKFVBBrWMKoNeQhcNlPyyzlhXDSjWY6xjlZSOFMqRh86xNqkTouXCgoRDybiXUx4cDaYvZI0HOocpFrIAEhW6wztSkZ0wd29RXa2e2TGsV9rlKACpUSyXIuICSGBfZQsQZhDam16NSDAaZTRiEv0dCN05ztMTowy1+nzmtP/km98/ZtcfOHFfOLCf0VbRc8L1ljmeiUNLbzkZS/luOMeyZlvPIN3v+fdvOov/pzR8TFqJ3zve9/jO9/5Ds9+9rO58FOf5sMf/RitRoN/fN+5mIy0TAV27NhBr98jazSpel1O+4s/546tW3jpS1/O5KpVNPKC888/n/HJSe66aztXX301P/nBD1lsz/OlL3wJL8KGDRt44hOfSCsvOPHEEynLkl7Z59nPfQ5jY2P8wR88n7oukWAYaWUsLMzzrrPfyUtf/jIyY3nY4YdhTBbpbSayQBEDKrGWHyA05YM5alzkn6SOXRhibcAFx803/ZI/ednL6fZ79Nsdjnvk0XTaJT/8/nUc+JADcKHm8su/y9TEOK/84z9hv333p93tMLtrmq9+7QrGxyfYvOUOLrnos+xcmOMbl3+NHfOzrByd4jEnPIof/ehneF8SRHHzrTfx/nPez2vPeB2qcoTUQsVHyN8AmuDTLPhNxpANvgw2LAK2sPQWA2SQe0vlHFmeVOoSpF4LeDzBW0r6LNajnL9xlq9tbdNUOtqKUKN8ToRKafSAvTvg0iSks2ghBA3exYUklQ0e6LEXznQDk+uYd0dzyjhxG+LwytJxHdY0Jjhmn0VCGRgPjm7VJx9tgLd4qckSmMZoklZDvPhWaWod0F7HFi4KFTxCEkeuA+MTo/T7Jf96wb/wZ3/+F+zYsZ1bN92KKRrgapQxhBpGmgXzO2ZYu8++nHvuBynLkos//Tluu+1mXFXSyjM+l9KXk044kSc84Ql84NwPUNV9rG2w6c6NNG2LO+7cQjMrmNu1k367YuXqFTz25MdGZCBCXTt6ZZ83v/FNNPKCS750KU94/JN43BMfxxcuvpQ77rydo498KA896mFMjE/Rqys+/P4PsGq/fXnOs07l+1d/j2OOfjjnnnsu73/fB8AoFts9JsYneNd7/p5PXXghH/ynD7F23VpUUPikFzHkEcGy//ntHplk1KqmMDkiURnRptR81/QO3v3Od3H9DT9n333WMzk5ztzCLNdecyNZVnDnnXchHtr9RUIVKDoLbP3Vdsqyx+j4GK6qcS7w75d8mS997jJUkRGqmqnJEY46+DCuv/bnaIE7tt3JmhX7Mz+/yEUXfoajjjqKpz79dyJtIdgICk69UJOpIZT4NwGbaa2piSrvjmi74LxnrDnBnMyQYSmNoy57mOZIhC1osAZCiGLPBRVzjHDmz7bRbaxgREWOlBWPEosPXYzkOCkxJiK3DQ4ngkIjOtYXlRogqVM08iDUQO57TLPs+g04iAMQGNbgJSDGcsPcLrYvzuOt45b5aEbckUCtQ6Q/h0jwUYmvIqmN65et4CJRKiYojUn5XVZYSleSFTlfvORSPv+5S9Fa8+IX/RFWKXqlp+r3UAYWF+d5xKMew6pVKzj99NM5+uij6fTarFy9mmOOOoaLL/4st9++hQsuuIA3vOGNXHDBBRxx+JE8+vgT+OIXLuVpT30aJ51wIqeffjqz83M85tEn8fRnPZ3Xn3E6tavJbIFzFY3MorXl37/8JT7/pS+xsNjja5dfxitf+Uq++tXLOe/j/8J5553H+eefj8ZQlyWf+uxnWLFiBSNjo8zMznHqqadGzVat6NWesdEm7apkamqKN73lzRz20CN59Z+/mutu/DnB1xEHkvLtAfDuv0MjRlSqcaUukk2KED54PvnJT3LjjdeTZ006nQ7OOe66Y5aRkbG4uViLCzVGKcqypN3p0W63KYqC+dk56rokJMRlszmCDyXeC71+zdbpndzxqzt4+jOfxmGHHML4RIssy0AL57znvVRlL9YkAEx0vA9GYvExpTK/aZvXyEBik+G81rlmdHwM730U1uqVGB0XT52EyZT2WB2gyLhk8zxt57EYejoqylkb5RUleIwCEYMosOTRTFEtET6jhsoA+SxEVORvQQQysN9DDUqd0X9Do6L2gGRYMRw+Ms7BhzfYsn2Wk47MWZCMZlWjTRTU9RIiH+Ze7kkUFibpPCS8iIodGDGCDhaFY2RslFt+eTMXXfQZFhcX4wllOY0iepEWoy3u2LwpOqJ7weYR8JUpKOtoVPS4J5zC/vuvZXrndrZt24aIp91uc9gRR3Hq85+LDhqnA8oHMmPpVyXWapQ2vPhFL2LVmtVUAV77+tfQaDTQtmDfZoNep8u555zLU558CqV3/OmfvJzzP/FJJlaN8/jHP5YDDjqQ888/nwsvvJDfe+bvMz45wfve+z7KyqGtodvtkbeafPEL/8Y3v/EtPvGvF/DlL3+Zl73sZYkrolLtCFRQKP3gEaYeyKEIhOQVaxI4MPiazbdt4N8u/QJ1XWOtpdksmJ9t02w2yfMG1kY+lWmNUIWCwmR4ATXqWbNmX6anp2PnwznQgrWGpm6i8ozgS26/63Yyrbj+hl/w0CMO59b/3MzqqUn6faFddbnkoot5+cv/GGUUlYB2EUgRBb31b9zl2o3FnAqzSkXm7/j4ONPdHkjA1w5XtcmyCTQe4xWV0YwFxee3Vly1uMiInqLOHCNiES00TEa7nEH5gLeaIihKpVE4lG5EpT8ilgkUJiQ1FxV9iR6MCGSvjKUG6s5CvMAbb9/ElVdezrqDHoJ3irrqE4LjJ1u34ivFbTPznLx6lBWtRryYVlGYJmOTY6w/8CAmJ8dZuXI146MjiQ8TkFpHBK5JBaEQQT9Ga7rdkpFWwU03/IKf/uTnXPH1y3nH29/JQYcdghdFI7f4fgUNQ79dUYw26HdLsixL/BUPlaLRzOl1SxqtnKrvsEZR1hVaa1qNBotlRRY0NreUZRlBXtbQ7/eZHGmyqz3P1OgEjkC3W9EsIgsU0QSpyLKMqhRMQXTi8wGVKVzZp1m06FR9bLDoDAprqVzUF6mlYiQv6FWLLOxoc/TDH85lX/0yf/Cc53LNtT9ln7X74/zSbdPoqMD2AAGhHuwxgPpHWwPYfNutnHXWWVx33XXMz8+za9cuJqYmCS7WSeLOKgSJCfOAn1RVFWNjY/T7fZRSVFW1xFsShVIe5yKZrHYlRd7k6EccztTUSrSKmBLvDJ3uIv1+l8su+xoHH3p4lBT0EZKeSTJnX85/+j+dnw7oYPAqtmkDSaYhVMzOzrLQbpMZg1ISQW9ZA4ynHYSP/nIzV2+dwegWodDkrX3wrk8DoewvEKoaraO4Y9/V5EWLbMW6tHhWEVOrIsgyhEAumruv+x7ffclTKCZXLzvKgX7r/Sus7mURdRArO2AQRUSkqdJ9gjZs7C2ycU7TqzpMzwtubZZEX1Vs/aoQAVr3JiAVVIQtB4/YpfR+2KkpDN47Dj7sUB72sKN58cv+iNtv3xKZlqGmEsE2cmpXMjkaneUnWo242wVBa4vLovBOs1GggSK3aK3IMksZPAYYLfKB/RS2VQwBz8VIEwFGGs14Tbww2WwgCsqqRltFQxd4oFlAKQGrwRqDFygaLRbLPq2iga8dubU4EXKraJc1WZ7F2oBpsd/+Y/zohz9kcs0KfnH9daxcvU9qmQtBL5vgw0b3b/eQENBGgzY0FXTKkje/6Uw2376F6elpQgg0m81hKqJUhPYv1/8wxnD88cdz44038ohHPIKZmRmmp6cpy5KFhYUlnpIJWNOkqtt4J/R8ydZfbWP1yjU0RxqsXb8v89vm0GqUbnuR73znOxx0yOEx4iVEcKOKxvB7apT8V2NgyzGI0UWEuuqzc/ruqJpuSS590FnssqAXyN08H9pS882dbSa1AVeBD4TqTiBKFYoI2mQRlq5NFMgeHYkZgKrj9R0cQ1pkkwhCtMoA0mx+wO7nXvnCLEkFptUrQfmcBDLRZFJhO8LWzhy1gxk9z86qyRobbRPMwBozJN+VEFOgSEQaYAOWYQW8SuK6UdOy7tXMdec57hHH8qWvfIVm3uS4Rx3Lhk0bmVy1mpH/zd6Zh01W1Pf+86uqc7r7XWdfmGFgWIYdNwSUNYDITRSNqMFEFk2u683VqzHGLUFZ1BjNgsl1iQaCiERjNIlRo0aUJYosKjCyyDoMAzPM9q7d51TV7/5RdbrfGRZlggl5rvU88zT02+f06e46Vb/luxRtFMXYFt06Jq0IsrK7eCBLvdFKTFiSQAsmsYlthCQ8H6mzbBwkDIOPSmEMtQ+UrkSA0trMMoaiTF2SEJWZMMOoGaJlDVPTM4wMD2EyiHe01c48HduP5Lp1SDmxgg9KiF0+8anLOOLww1lWdrh7/d3cdNOPOPrYE5BMTY+haXH/9yDUBRNx6pJ0oYUf3/ADrrrmWuaNjyayYVFgjGHjxo04N8A2GGN4znOew0c+8hGWLl3KkiVLcM71c/7t27cjIlx++WXce+967r77Dv7+7/+BVquiqiq894yPjzPbVX6y9qesWLEYT4XrdOgYS9Eque2229hBRD87GEq2c/h5FuikXJZSSy+SbFcb4iigpiZqyWipaITaTjKmBV+bMNy0tcuwtOkYxdtAQZJ0EGvwtkBwVKGitBBV8HaYIduhlllcKJONBiEZx5Pmo43ZiyjLYvyXA8kEO8egpkEQBiJQOqAuqLWi1bYcND5CN0Zu2tSjQ0nhDJUqURSHSahTIqNmQKYzGOrsmGFIZjzOpGKWYJAAZctQFItYsmQJH/2zC9l3730YHR1lbGSUxgbBB0Fjxde+8jU+/7nLOONVZ/HZz1yatCw1cPDBT+fVr34173zHu/nIn3+ED1xwAYsWLWLRokWsXbuWd7/zXVz4lx/lwP0P4N777meffffipptu4n/82gs54ID9KJzjpb/+6/zBO9/B5z57GaIwNDbC+Pg4b3nr7+EDXHv1D6hnuixbvoi77rqHF5x2Gt57xHs+9rGP8apX/TZf//o3qGZnWHffPZz2G6ez5+q96fkaq8kzd926dVz6mb/l4x//OCcedywf/tCf9EVlCIlUGHXgd/NUb+PabFkgYrDATbfcTKwrNm3eSLscpjHIGhsbQ1V5xjOewUte8hLKsuRlL3sZCxcuTGhktyMAcd68eagqr3vdGwghsG7deg466BCMMVx66aWsW7cOgNnpGTREVqxYzKLRBdy/4QEmJiIzk1P88Ic/ZGLLZkbGFyKSfGBMSAZc9ueM7pKrYvo9HBmbEYUemhX4CsREas0S0NUof3X/Jv79oWliIbSpk8CVCiYGojEQhUI9IUbajgR8K4bpDA0jxiW5AAuxTsXegMdqQdQKL0VCwyZJf/6jKcvO44mnMGhT/eh/UYZEg/c9gzUzRAzbe1NMSGRypmblUMn2mSm6wy0iNUOmTDiQ5hsPjyT5iBg8ASOKGpOsOYCgFRFHq4BPXvQJXvGS3+LFLz6Vd73n3bRHhimKjEJVT6dwLFu6lKc/8xk8+wjVinEAACAASURBVIgjePazn53ISrbktf/zNSxespSysFz13e8yNDTEJRf/LQccsB8HH/Is7rjzdtbfv4F/++a3ePjhh/mj957Deeedx8UX/w3HHHMcRx55OF/+8pdoD3U4+qhjOf5Xjsa4khOPO5Yzf+e1jA8P8b73/AEXXfJ5Fi9eyOtf/3pe8pKXMDkxwac+/Uk+d8llLF+6mLe+6e2854/eyeFHHkGn00mKXKZEDHSraU563gncftttfPhPP8LUzDTP+x8nolIk4edmhySZRBmrT3nVstSQt31F+k0PbSQoBB+pbYXLNh3PetazOOuss/i1X/s1Fi1atMM5nBuIQTWPg4UzsaNXrVrJe97zHgB6vZpPfOITeJ/sRNpli5tuuoPWkDA0NMLsdKDb67Hl4c189atf4Tde8VsEtRlPEShiVrj7ORbnpv4BSb29MFkkWgpElEBFyTCGmgkP56x9kHXVDIucwQdBCsFostlUm8ASUTU9HyxdasrWCJ2xxXSlogqaOjJmwCMzWQwLTcTOgNL3IHmS95ddwIFkso40BHv67nLRdSl0iCE3y9PGlvO0BQ8RqxaT08rmeoaWiwQ/RDRKQVLQLkmhV5oAJuNBQr87Y3CEmOwioiqWVt8k57BnHMnXv/5V9l2zhtmqx/evvobnHHMsXgOdomTr7BR//Mcf4AWnvpDfefXZ3HbbHdSzNZdcfikhBG5ZewsHHXIwxx9/LL//+7/PokWL2LDhIX7wo0/zjne9nX33v4YvXP45jjnmOK644gqOPPwIjn7uUZx48vEUbpjR0RGWLF7Gl774d3z1X/6ZDesf4PTTf5OR0Ta/97/fxBFHHc3uuy2nqru8+MUv4awzzuSjH/0omx58iE6rjWu32H2PlfzOb5/FVA/GxkdAYdZXDLuSKlqOPOJoFixYwN9/4ct841vfZtHClYBiNXGGyqjUJqVnQeVJ3l+e/NHIFZaahH8e3ryVVlEyVfeoujVaRM455xxe97rX0W63d+EdBsplMXqMcbzvfeewbNkSzj33XGZne2zYtAFrWkzPJtBip91htldSuoJvfP2bvOwVr8RYpY6Bjjh6T6C2ZNWlNFmTU18ykE+Ma+ccIxR0HXRc4G/v38qD3WlG6dC1SXgab4gukNzsKiTfJ5YWWkKrnI9rtfHYJEAUSsBjok3+TNpKzowClRS4mEypJDa3+qMVT3e9mLpLRdQBZim/aVapdlLio2e27nLP7BT/evcEi61w6LyCazZs5wWrlmEKEO2hkgqQTWGs8clVMX0LhiSBn8hCHs0ITPChplUUBK3Ze80aej4QqsBv/MZvcPXVV7Ny372IwFhnBFs42mWLqZmKz1/+d/zuG/8X7bLEOMuGB9dzwXnnc+KvnIAxhkMPPZQQAhuv3ETtA5decjG/+3/exNVXXsVVV11F1e1Rh4ojDj+KstViaGiIlStXsu6+e9h7731TKD02zlXf/g7/+KUvs+aAA3nhC34VVxbgI2tvvYV//pevcOihh/Ltb13BorEFXHfdtYzNX4S1wpr9D+Taa6/Lcv9ww79fzU/vuJ8zzv4t/vCcQ6hqPxAmzkbcyQE+q6bv0hT4zx1RFZv7zdfecC3Xfv/7jM0bZXJ6G91uxdlnv443v/nNj3uOnUWvm+dgR0atMYPp/YY3vIEzzjiDk08+mRt+eCNWImVZ0qu7dEaHGe6MgBFuvfVW7r7rdlbvtQZIbQJodvefZ2gfMi5G0WgorCAmsGy3lQQTGVfhygnPlaHDsmXz6ZnN4EK2TrXEmLtUGilDkWQiRBFXEOqYjKW0xtfgqCD7VLskfpEJfAYx3VRoyv7Qg+jDPMbjEx+7cKRg+k5eO8dDiYRmbYv5dgilopSCOzZ2aVnFSE0IuR0qWcrNNO5fA10RIGmByIBlajK9LtQRsalLMdONvO0tb+HP/uTDbN22mS1btnDVNVfjGg5kDHjvUVWmJ7dzxRVXsGXbVqy1lGXJySeczLOPOJztW7exbNkyjjjiCJ72tKex2/KVbN+6mQMOOIh//tI/svseK/m/f/lXHHDAAdx11324VsF5557LTFVz190/ZeXKVURqvPcsXr6UI454DrfdfTfHHnMURx11FOefex7Lli3j29/5LqeffjqXXHIpK1as4Ktf/xdOOukkrvrulbz2ta/nxuuuTzqYxhOC5+Mf/xQ3/vAHbFi/nrf//ttYe/OPqSqPeJPEiaLp6z/8dxmJPp8EkCcmtjG5fYJut6IoWuy777584AMfeNTjGkX1uUrrkLFBOmhhN92anY9RVUZHR7n88ssZGhqi1R5KoMdg0RCZnJ5iZmYG4yzX/uBGRFIxEqUPIvt5Wrj9hYzsFyNZQV0Kajzew4dufZg/uXkjbSKTup7oBanAVQYblLIWbKXYGiR4jCEx16tIIVBoUrYrMYh1fQChyKAZYYzBZvOqVEb5xcyRXVhAUlQAgxSGLM8WI9QhK19Lj4VlybJ2i6roUXulihaxKSURBcQTQzYvloHPRhI1Bm8GIsfSEIFSi4O6DhgL++67L+94x9t55x+8g1eeeQZFUfCD676XXMCMRYJQtjq87W1vY3R8jNe+/nUsXboU51KletvDWyiH2mzcuJFLL72UL37xizzwwAMsXryYPffai+XLl0MU9thjD454zpGIMdReede738OCeWOMjYzy09tvY+PGjZxyyilcdtllDI8M45zjjjtu46STTmb/Aw+gMzpMp9OiWyt//JEPs2TJElavXs0ee+5F5WtCqKkVgldCtyJUgYnpCZYvXs7TDz2Ei//mIvbYYw8winGJjxQkpJpHntz/HXAgKeJMNgSzU7PUcQYjNarC777pfzM8PPyoxzQLQYPzaCQAms2oef6xXtsssqtWreKrX/kX5o2PUmYkcTWbbB+63S4At669hUxxS1q0GuEJtHEbXVfrapyAM6H/3Cfv2cyXHq7w0TChgXYYxbgELXCFSZKXpQPjKK1BSpeAYNZQusFnRs3gO2COuVkWkLYxoX2dsSA1j99B2vXC2S6Yazc4Dmjk0xqVsmgVq5aWKLOzLW6dnGL+AmGBjDLLBM5UaBxGJSFRk+xbgtzOnfwmO6TZmEFr0WK0sVzUZLmAY6gsOePMs/nGt77JP/7zP3HjjTcyMjbGM5/5TD76F3/BSc8/ifZQB1M4Tv7V50NULr/sMi688EIeWr+BT3/y48x0Z3nZaS/lN1/5CkZHx/FVj6CRq666ioXjY1x/4w2MjIzw1re+lW9889947nOO4I8/cAGHH344W7Zs46Zbbmbp0qVseOhB1q69ldnpGbZt2sT7znkvGzZsIPgu5557Lt/4xtf54Ic+RO17rNlnX2Z7XS78849yzvvei+9WzE7OUmtNuyz44Af+lIMPPJB3vuPdXPNvV1DXgc///WeZt2AhPsaksobNeiAD3YqnXCQiEYl2kHZJaqmrgrVp3sTaEnziavz6i17cv/FhkKrM1USdm7Y82udtCqyPNw477DD22Wcfvve972FMmy3btqaWp7U455iamkp2lrlyLyJ98aGf9zuOmmw7jEac8TgMV050+d5DNWMxMlLmFjUBpw409MGAogZrIebQwmZ7kyYaDyiSlfwSH2vgWtAQAKMNOEoq7SFVMqkfLBQ7xw27DirbxS5M0kRtujEpPzQYcdTUVK5m1vZ4+tg4bVG2+IqlI20q72gVVf4oj2SPzk1h+tLzABKpjUn2gVZp2USrD4ApW3zmM5+h1+slHEG7wHvPX174UU543vG85rWvZb8D9wMM0QkHHnoQw+Nj/OYrz2Tegvm86MUvJaBcd+01zMzMoJJkCPdZsz933Xc3//rNb7BixXJuvP6HnHPeuYzNm8/6dfex7eHNXPq5S1m9ajX33HcfI0NDLFmyhB/84Ae0R4bZ75CDeM+557NtYhOr996PT118UZJQdAYvystPP53nnfI8Tn3hi3lw00ZecvrLU+3DwD986e/Zd++9OeiQg1myYDFnvuZ3WLBgEb0Ycp8wwbpTuy8zluWRbnz/VSOaiAmJvzHXD63P1VEPwTFv4QJUA8PDw4zPn8duu+2WoOhP0nj0GolireOss87g+uuvJ4Q6YXusS4phMbL36tUYC8Gn77URsJ5bd2minR3mr5G+UJYo1ASmolCK47P3buKfHghYAqVLc98geSWtUTOwbw0kJfbCJHP4YAaSFv3feE7Xycy5NmjUyFJGYNVCof3jH32R+E8soposvybkmoyABAWJaKW0xVKr4dAF46xZ1GbdA9MctsTjfUk0nliBugTFbbw9RGxfEsAAoY+Wa2Ic+jAeq5aZmS6dsgXqEx7CGs4645V85atf4w9+/x1cfPHFnPOHf4grOhx//LHUWYg5qnLooc/kmc86gugVayTJ5gv86q+dSu0DxibVdFU44zfPJAIxRI485jh8XWFtQWvNMKOHlkxMTjHcHmH/Aw6kDlBaZc1++6PAa177ekIMzJs3xu5nrqZlTV9HRUPgV1/wwmQ6FGCPVbuzcvfd6fqKNpahVpuTTjiR97z97dx4/Y/5t6uuYGZyBjvsQC0tm3xRE+YlJmV24lNi8YBMaZfEuk4mXYONJrmuOTSk9n1ZDlHVUxx55DEA/Zt4btoxt75h7aNbNPaFdOIANTqXWDaIYhJ25pijjscWLkkHzs5ibNLViD5wwMEHJP1b6zBAlXvjjxZ9GGMSizfXH6xI8nsxBaW0KKTHG26Y4e6tkwwPCzVtqMC2HJE644dNBns1EUUW/g5ZYyd7uqik+yCaiI1pIypweMnq/r7OUPYaKQqc9hAcrTjQaH30Nu5/YhFVdbAi94FkVhCNGBvohUgR4IYt2/j49+/k03fcx8W3rOdLP7mTXhXp1l0k2IwIzeeUmEl6DVovLReNoFDzqKrUvsdwp5UnaUGrU9Bptbjo4ks59NBDueD95/Gdb/0bF3/m4iRRWPWwRpiZ6RFJvi4WEvNSwOfJ6QHnEoIw6z7jM/aktAZroN0qEScMtxKytTM6ghTp9WKTaXId0g6SyMnZ1AibRHLrVD0yGfRVxERETFEWdIqSqWqGD73/Qxx33DH85V/9X55+2NMJQRkaH0aiYESJPn3/Lrez9T+Qwz7ZI1kZpAUNTbouNCSAbGXR+O5s2rIZpEvViyxfvgLgESlL81wDYX+s0YevG4P3vh/JNOcbpMjpu9pj9Spe/tKXUdc1VVVRB4+vanrBU1qX9FZMyHNAdzpHXqiygLazNqX2JGxOyyaCm/E1n17f5d7J7bixMXyVwOTlUHIESPo3AcTiVLKmR7JGKfJncaSSQSqQpnpPSxMC1+WIw0SDupz6SVL5S3SQZHKmTYqyAwo17vS4a2MXkKgkhqEKUULqP2sAHEaUIddmsppiqOzwzOULeWhyikMXWx6c9HSiIZQdRGsMwyl3x+wgcZck8QMYl4RpSQXDmHeR0rWZqXsM2xYzoaKQkoDSGupw1TXXcM13r6ZsW1at2jcpSwVDr4602y00o1m7Gnf0OI0JbhxzOzr5q5pckDMJRRibfDwtEtakDgDZ/yU5aWrWdxA0RLwKNiYLBq3zJAwZWhyFqVDTKiy1J3VTCoOTIZbtuYLTT/9N9txrd15++tm4osBXAWuKfLzHSEHMeXnUhEB8KnBhrOaOfOaCpIg6pw8xyfk1Yfiavdeg0eJ9xdFHPxeAXq/H/fffz4c//GHuvPNOiqJg/vz5HH300bziFa9gZGRkh+hi7uPU1BQXXngh69evZ8OGDQwNDXHaaadxwgkn0G4nRu/cxemNb3wjl1/+eXq9HmNjY4BhqGzxk9tv46hjfoUYcpptLMHXGJuUz2s8VhOexYlDY0ibnkDtIwGhYyI/3jbNN+6fZsQVdKe2U89swznDbASrAY9SiCFExdiU2Isagom0o6NHTUsstTJnARMmo0/wTQVvheGR3QgRjCQbCE8A8VRAS5NnjJGmlTu3DhIfJyr5+cZ/0BcmJqUjEoHDB8HHLioFozLNV7dsxc5ahikZNcJEx9MJoOVgNe9HHX2by5zmx2Z3JeMc0kSpvFIWLbq9GiPZW8UJoRZanTZHH38cvVhR1zVFx+Grmla7oK5C309VVPs5ZP864hxMinqiJA6E96knT7/Cn7tCMa3oGgzatKI1h+8xAZ9j7XFlkf1Ym05JYoeSd5FUrEvIQ+NTqrVwwXLe8Yfvo1fNgCZ7TltafJUg4Co2k7HSzq4mGZU/FcZcMleDGZo7Ykwpl1phj9WraXXayMQEa9Yk3EWr1WLBggVccMEFtFqt5FxoDFNTU4yMjCQ5hqLY4ZzNe42MjPC2t72tnwZNTk5y8803c/3113PUUUelxUMH5MzNWx/G5CuuqirVpxDWrFlD0IhYi/qGxJY3ChUMKSKwCg6lckIreGJmkhcYPn3XVv5h0wyjUjBjDG2rTEmNVBaJKZm1qmCSdzIhX5cB8RW1AFpTixC16vvLEASDx6hNBdK6xM4LWC2JUYkaU3RtCpzvoWKxjfRoH781p+YhO/3/Exy7WES1GYlagA4EhZwzSB2YNRFnSk5YuZJ/+sn93D05xfG7jdIG1AWIBabYsdI+N7xKJtaKNwmo3Te1BpBAr6po2TIZRklKDTqd1LN3hUOiQWKCv1sr1D2PdY66rvvVf3h0ZfCBhwb4OuKcm1OBN/nGbVAphqAh9ejnnKs5TzKcCn1yYNNeTPL+MUU1VtA48D4tnDA03MYZsO0WqhZrofaDXWjHItpTo3DajFQXm6vctePFNVFcDDA8PMz+++3LpoceZsWKlMLEGBkdHe3XQOYuDqpJS3buuXb+77nt3ZGREZ71rGfhnBukPzLYcI9+7jHstc/ebJ+c6qdIooHFixPt3UKu3zDoDuWiamolpF08UffBS2TEej59zwwX3R8ZoaJ2BYXPaUQNSTZcCTFB22NWUUc9qOA1oGJQCagaMBHVhokzKJIGG5LBo0nztVH0QxXNkR4WXCzREHIV6vG6MLs2du1IbXw20wVpVgT30fRFkrfX8OU7N/DKvRew78IOt2+pMAaCd6iG7PKWOR2ww81nMmzfxkxMCnNaUGooTdlHqtba7CARH1KEErwOyHmmkQbI15oXi7k5dWND2fx345lbFEV/8VCTIPzp2HTeKPTPYbIH79yJ3CwWc9WyG69XVe3/bYc2JRkLEiFEQ+09vSrmwm4DUnrqjsdFG0jzmggWvI8ceNDTmJ2dnWM1avqpxtz0pCiKx62BNGMuHiQJE3X6DN98FTT4UhHl+ONOQrNt5Gx3klbZYfeVqzFqqSNJYZ0di7Bps0sLuUNxsUdBRVlX3DkJ/7p+mhE3S4c2JgitokbwibGkBpFE6IxRM/DNEzVBFrT5nWOeb6QGgxhPimJJRdVgkq2Dj1ihX1NpvJ/VkXR0TI2Ig5jZuDT/4D+ycDTjiddAFKLJK55kHEi0iFFcn4XYZX4ZOWJFmwktGI2WfZYOpZs4REzZIFmzhsEcGHKDO4Wmc20wRgmacr5m5zWSipZCmg7WNDfpYOLFEJIITT4mTcBc0IP+6x+t3QeatUPS35P3xo7H2lzzGBw/6ATAYBFpFitVfQRrducuQV3H/nHNOWCAvITHv0mfyqOpO8WgSUTZelbvtRfzFy7In28QSscYEzLUmL7txc8a/dZ//j7nYkqa5/vShJLqYdu2bwEGG8eCBQsYGukQQvptQwCZW0CVkLRbjCFID0yZwJNB+frGaS6/twuqWALWlggBo45CarpGkWCRWKboQxqRgEGbOMaIS0LBebGxiZcThEIgGAMBXJE8X0SVqBY1ib0rkgpMVsG7iKmEYJtC9pM/dlHSkPwD5JtZI0RNni9G6UXLuoltbN0e+dHk/dTbYb8lJYctHU85bUgy+pKrabaJPiT2w0JVk2/1OX12SH6wmgpLFpI3ba4liDCoDUgK/Rp6+JxPsEN7b+68TLUNTTICEnEyWACaWrYSsOKSU56m1GPuYvCUA3M91YYkVJTH05YWYpQXvfAFuWuSBZo01Z6+/e1v84UvfIGLLrpoh4Ip7Ji+zH2urmu+/OUvs3TpUo455pgdXtM/RgaRzMTEtqRu5muWLF3BmgP3IcYEJTCS1G9kTgTpY4JuVDFQiqWONQWW9/90E1c80GOsExCS9WmCNxhidIRCGFm4Gi0C6oUym74byZ2SmNjUTdQqGojGYqPgpaKamaWamsTFLl4c2MjI6EJsq02v3ykCkThYdHqpjmPFJeOqX8DYhRgm9qOAPvAl99DRgFEYEc/YeIcfPbyZVeUi9lxs6UhaLQsjVDY53adzFP08EzU4NOFMRHK1JS0qrsGDZAvMIkO5bWNoLZKNhA3WCRFLdJ5CUuW5qf5bN7DORCLYwe5i8w6pogNdB2exOT3p4xuMT61sabofzPH2/eV4vNEUy61agga6M7OcfPIp3Hffff3XNMjP6667juuvv34HwFaz8Hvv+zyYuQjRuq6ZmJjge9/73g5p61yYO4Cva0IIrFixOyEE2u3k07PHbqtwLi0AVi3WJdxFc56WNRiNdKyAeEpj+ObGCb6z0TNSQqiTfmnLu8QyV5M3yRJnBfUWUcVjCcZRiyVSErD4WBC0pIdQ2TZeUuLjpYVtj9EeGwdX4EqB4Ng+OUGqFiaYexKZKpMNiipKg3at+hv0kz2eOA6kiUCgD/XtmxtJQtV5cUxvrShaHWJ7gokY0MISjCcaKCXpRGJB5wB/duZzJM2IJD3YeOM2xtW15v43TeqTFi/NvJyW1dRKtaSbPmYrTZ/DW0lIQAd9IyFlkKcb0SSK66v03iESi+RrohjEhT5ZCpID2X8HVfSnwjDG4PF0u10qXzM+Ps7dd9+dIsyQ6mvee771rW9x6qmnUuebva5rRIQPfvCDPPDAA30eDAxSvBgj559/PnvttVef29IsHqkImjRTiqKgrmtOOPYYrrzySsaHx/ExcviRz05tfQEkYsVgU0Ghnw4500qpRoSN3Wk+e88kYybiQk1pJAG3HFgs6gAi6muqDFdIAEpJ/jMx88pMelSjUCvi0/kBjAcjJaY1RDE8TPSKhB4626U7sw1HFgwiEmNFjJm9m7EjMfq+/sqT/ls+4QPU9i0AB3Ayn/M1xWqLmkjXKEPR066HWLdllplJTxmH0gocfD5XLroqOQURjM4pVqE7qbYLNkcshRiiiYgm3EaB65PwahF81j4toqLUWCcYG1EXieJxJLew2FCd89frSM5igkOLSMeVKIF2kXANVlyqZoc0qRLZr3FE/+UK8rOGkNLQQookvt2tOPDgA3jwgQ2pXZ1rRSMjI3z605/m/PPPpyiK/gbz/ve/n9WrV7NixYr+wgKDDtXIyAhvfOMbOeWUU3bQE2n+Lhgk60W40vLt71zJs571LH77t3+bZUsWcdjTD0MNCemZ8ULGdLGmAIlpfmgXQ+TrD03zRzdOMJVJa1FaOMk8LlLhNgG60qZlSGmxl0BJBPFYE5OeqVWs8xQ2gqtBamoCKomNi1SICbSGFiZzMWMoraeemcJK6G/iZo7jXUOuS3PzF1MF2SUcSFMD0dw+Ql1iiSp4p5jKsbrTYd+nrYIg7D06w3YzCramsiU21ymCNC1NTag+1SSeokLIokUqKT1ympSdvORaSbQYfPYUDYT+CutxklB90QXqIMRCKHwCp2nMimpGQWpCLPvOeAEShDTrKmgUaq2x1uDV4/IiVwebokELQfMqLHM6Rb8cjzma7lmIgaHhUR58eBPz5s3j3nX39dvcTVSxZs2afrcqhMCXvvQlVq5cyWmnndaPKOZ20JpI4y1vecsjCqjN31HwMVJYw6YHNxJCimrmzZvH0UcfnejvavGNfARKkJIgAZEs7mwsVz/Y4y/WTtAattjoCN5DYUkmTqlLE2PAuTJtcNngSaJSIqg1OClI1hxhjri4QUKN4DBWsdnFz8aKWg1F6RALthaCsWgvQqwR2kSSh1IjN5rgDxmiL+m5J3s88TMKc8BBuQVLk1tGnFccSjlUsPbhyF1bJpmoOiw0veTFEsA3zMBo8WHQwk0h16BaTi5gGW/66YHNOZ+I4ExBdJHSdSmNYqmw1Ji6hytqymhxtkc7epzp4ZilBdTGUGjAhZLSKB1nsNZTSKAwkcJ4agkUxlP4WdAa1NEjUsUaMTWxmMVJk9IJSNj5m/rleJQxF3fjbIkK+LrHtm3bBi3zOQVPEenD0m+++WZe9KIXYa3doTU7NwXeufsyNyVO9YDkxxtC4Ctf+Qqr9twDEcu6e3/CdSO78+drH+QHExVbVBkiUBqlHZRRjYzgcMbSCoEvPDCF6USGg8NKoN2ytEVwVnFWKBwURjAScoqRkCNiTTabCnnjSpCDEEnwCDuDlCA2ojHrD0dFbItCIsEk1kxzjJiARgPW59RlFrEVSkVbIkE0XZP8YnKYXbN1aP5H6MdFohC0QIhULmJ6hrWbN9FptRHfZVPPcvjikhg8ZVFkRF/M/e3Bjz1X+MSoolJgnBJDXqhijcERStA4i+oQF/yox7sOSsWrL947y0mrh5lfK103xXc2CG0nHLW45PsPzTLUgb2GDD0VopslBkMwLrdshdmoqASMtNgwM8t0LazdMMshCyrWbgsscMruQ8rK0TFqklmy5ojnl+Nnj7k3NBIZHx3j9tvuYnp2hl6v11dah1QPmYv9aCKRncl2O5/30QBmO1+DWMPNa2/huGNPJIYe377lHta87A+4csrwzVs3o90uy1uWA8fbLGp59h4d46DRFlNEPnjLJrZ0BZN5LOLyBtp0J3MkJLYBzmW2dATVkDqD0eCJOE11CpUaXOMX7BEXEB+BEp+xUOIc4mucjdQiWImIT6RTjGLLRDERUus5mEhLFRNLflHb2y5qog766PQ7F4ncRQ02Rr59/zZabUPsdtncm+Xo5cupvSeWSa26yLgKGx75wzf8DoNJkrCJSphCQFMSpabtA//0YI9P3TbBzdsiVz5cWAq3rQAAIABJREFUMWyV+yaUv7qzx5sPXMAJSxx/8IMJPnb4KH943QQ/mgiMmJo9h2peeeB89i0tLW/57sYZhqNji7HsOV6zvHA80J3hozdMMlkO89Pt29lvrM3tsxWLjGX3wvLO5yjjxiaoeYMCzEzPX47HHwmKH7FYzjr71bz011/CC1/0Ah588EFWrFjB0NAQkOsW0Fdgf/WrX83ZZ5/Npz71KRYtWtSPOB6rxZuez0jPuc8jzE7PEGPkpOOO5qJLP8fosS9A2h2G22P4qS2olmzs9dj8cGSyVkzcwlRZsaRuE4cDhTe0TAdspGWEkAvpMECthhAoCjcQEM8jiMHYpBeCxqSybxQTBlVFgxALxVDBHNlPp5HKG6yp0FjiJMklWhy1DdgKgtFUTxLQ7KGcAC1P/m/5hFOY+ChxUGOu3YsVWnjUWJ69dJQR16ZTdtjYm2DjpmnKQsBDk5o2oJdH1A5iqokka+2kATJYVVN1OzqHzFScsnSISW85dZnj+cs7LCsdL14xxORsl3WzFdQF+y+yHLZI2HMocOBoj2esLFnipinqwNe2dFkwYtjkZjA6RW/Wsy302Hu0zf86bIw9F05z4LxxHprpcuK8Dq/dZ5xjV5Ussin8FJIxsqrifjZQ8v/7oaqoD1nzE+bNm8eb3vomLvrbi1m7di3dqtd/7dzoIcbI8uXL+3agc9OUpu2b2K1pPjVzcscpPoByf+tfv8KJJ54IFv7kry5i1am/TlVFpmc3UkgLKTweQUKPcdej1Tas1IJiKGIrBy6kVN5WhBgR6gQ7l5D4WSamFjBVKtpajxRdKD2FdJO0ha2xDtBAWadURUOKSgBsCBmRqhgKOrZgdmoqvUYMRamIyYuVgqlTvcSJS3KMscJIQroaKX4hKcwT78Igffh5QyPvc2GkhQ/Jba40kS2zgWs3Psxz561iv+VC7Q2ltuiFhDiM6hF1OawbTJZgyCAt7YN4IBW0jAQ0F1z3WTbGfvMNXzx2iDu3tVk/Eznn2aMct6THfmMl/3z/DOPDXW7bOstD08qGCu6vx7lla+S2zW0mCthDDRtnI8cvWshzl3e4ZzawvUqr/UJjePHIMg6fP8uS4VEWDRsuWLuVAxYZqhCpY2r9+jomz5tf1lB/5jDGgE1lvqgBWxY8/3mncMrJz+f222/P5LYB1byZFdZarLUsXryYN7/5zTsgfpu/A7kTZvLjHPRuP8XxBF9x7fev5rnPOYJLLrmEA970bqQuKBy07RDINlrWMFKWdFpDtJyhI56yLDGqtEpPaYTSxNQ+tTEX5T2IR+mBRIKpUnNAfO7CjOB0mFh0MKGDMUOIjhLdEMEWqGtjpIU3DnWGunBILCjcEE4rZqa2E7tdMA2uJdNaNUs7ekWNxavvawoLLvlqZ3Dlkz12oQYSMH0kn8no0byUxIrSKBMaQAqeMX+YE1eMcOfGSdZ3I/sOg2oXK0XSQVCDSopYUoEr5BaYxUuGgudJpAAmFY4cCey134jlvddPcOKSETbEaYZmLN9a1+Wbm3p87cRxfrytw6wP7D5Wsj3OUJtRhlyPVSOOveYbNm/vMVuU7N0usdJjNsDCEUMI8ECvx+X31DiZ4er1yrpuzbIi8pI9h7niXmWP/YU2gSqCsZauD7Sd2dni5pdjpxHQfiszYSAsWgivecPrefOb38wbf/d1O7xeIXfGeNQ0pRlNRNLXlRHNDOW5QDOlisL0xATX3nA9/3jZJfzDXVPMf+XTECqKnifUU5SSxH6gwGgXJSbAovYSVUJTOq0EjLN9tS9REI2JrxIDJgfYEaDqUk0l201RQ7CG2JDsgBrQBoEaBSRp5kQVagWpa0KcSWCx1MoihkBraAxvitRssAki6cQQQy5QRo9VS5S5XKAnrxuzCzUQSyTQKLNDI3YC2m7hpwPO9JiIyqZ6gnp7h3VTNSevGqaOhjaeXm41WUmqS43WBmooxNKV1H9PyM9EkzciSEwAMB8jLWsxppdbvJZZhSLOIHYe7VjQRnnpnoZLbt3G3VNtPnBdj3Vhmo5YnOlywdP34KglJVu7Pa7b1OLEFQWbZ7uYqoW6io6FH22Z4ScbPSfuPsaBiyu69TBX3N/jwiNbtDDMRCjFQYR2jkB+mcU8/nBIgoFjqSUZL3XMMKv33ov54+OIOoImz5iGZTooLCXkbyOxsHO3JsaY5ovkSEQsDGqbgEGrSS665G/5yY9v42NTf8eq8y/DSwdTTRKt0CHt4NaQTboiSAujiWRRakx8WIFKLS0PWd8pYZnUgma5wuAQmxYYIRKmJ7GmoI4GY0HrLs51iNGjRqg10sLhqTDSQulRaYE1WT8YgxNHT7u0tMRbpT00jpoSF3wCRwZHECisMCuZPZwhFo+9cOz6orILimRzE6ks2pEXAJ3p0XGg3jBcOpaMLeIb6zdz7UMbuXbTBFY9Hoswk9p3KFZMPwIRkSzKk2oLaiTB40MS3VVNFHgl4ANsqQwzleGWmWm2bPNsnRXumIps9RXTUmCipRsjz1xQculxyzhj1RjvPKDD505YyTHLk5m1C4bdRiM/3NJjIsDWXsWy8SHUj/CaA1os7LTZ1FVu3VKzYbpiXW8i9fujpczbiwr0vFD8soL6M0dUxWSrkkJiAoIZKHGcfPyx/Pv3rsTPTOUbP2GBYkyvu+GGH3LHHXf0OzUhBHq9HlVVMTk5yV//9V9z209vJ4aAkt0DZFA7jNqjVZZ84fK/Y/H4CLud/R5KByNmlpY3CUEaQaLFBIOJvRwxVDiS3YZqkl8gOJwHX1uktlA5xAsSbQJV4qhLxUqkVUKtSTNYRLBOsRqRVovKJFGg5qYPJN/oxNUvKCTBFkxWJatjFxFLJUpraB6m7SjoodQpTZJAaQOUjiJGnFo0NyNSOPdoqmS7HpHsWhdmZzpwNg8WJ8zUFeoM6ydnuOG+jSzstJith1nk2qmnbU3SU9DEGIwmh3sMKueQGL94T3AF1gXwTScmke+sEdZu7HHEsqTW9cXnLaPX6/HJe2bZbfEQ126Y5eAFQtsN8ZX7AjdsnWTDTOChLvxwcjtHLSk5cVGbvZcOU9c1D3VBKHnuioCo4to1h5QtWq1tHLLY0JFhZkMNcZS6q3SHA6WkllmF4oxSxdxv/+V4zJF+d5tV1LI4E8pUqDnltBdxySc/zdFHP2eHG18k+eDsuWoPvv2dK7jkkktotVr0er0+0Azg2GOPZd+99+m/l9kpHhRKfnTzDdzz4AQHvecChvc+FOcKZFYILqFGkYZakYBfqMGG5NdsTVKUb0SMxTSw9Dr/f5YZtEkU2WHxUWihFBLp5fTHIKmREFMhXjU1CKyCFgaTtUGsJDKciwFcCeoRKRCjtDvzkJFWcjAwkgVzQuIJWgG6OBGCKBqrvpbJjovFfwGdX3P+lR1boHGSU80rJ1it2LszxI3DLUbVI3WbvRcI1C26vqKVFaUGanfJXLvx2rXkApkkbUmJyTAbmmJQ2n2evajF0xYI4y1htq4oh1qcP79gNhisGIzWfO5X5rN8OHDqqhGssVTRJyUp65gJFe1gcW1hn1ZCtnpNGhBWEuT5707YnXbsMRMijjHi/tOEELFGEbXJ91RT+3YX5d3+vxqqSq01hUlclLIsqb3HhR43mPl8c90Ub5RIQYYJQO50Gcbmzee0007jtNNOe1Tmc8N9wSjqBVzsW5BAikA++dnPc+Tl3yROBDA1OAdxGmOEUpMmbjcmaoTXCotQxRprC7SBiEvCJAnpZo+S0acqeASNaU81sYst28R64HnUqKUpKR23dmDJIFl0KqgmwKSk/Cs4h9FIpMA6oRwdo+wMU2nGimhipbu8kScLWKHOaX9hbCaQ7jya1OU/UZFMmopmBrZJhpj3EYREStPm6gfu5f6t0yxpW6qyx52THfYa8dg58YtoIEj6ACKCNI53GVPRKJHtoEiW/26NRZxh2LeYrGGkKIFIFR0Qk2EzwoJ2i0lfUxql9hYxhq06w7B3OFtgMnkpmkgQg0aL4qkxRFPS0kAXSyEFs0xSMorLRkMhxpTzys6p3WMPNTIoujXoSVFiIztnNQsoPXmjX4T+Tx2C4hOnKAsxKYGiFFQdVhRja2wsiSgfu7vLNVNTxD0P4kuf+Sy/deb/JEqNkQ6qFZgSO4c5DUk/tSmelmVJu93ORdeAOCGL4AKeOnjW3vJjru212GO2xnYCTiKlV2pxiA+o8fn3iASjuCAYlNJI9qfN7y2xr+tibOjHOTFC488TSDqnhUTUghHLyMg4TRQ9V3CKqH12ukexVrExAcEKCiI+8V9cAbZMHZ4qYMUnvRxJm14IDQ1AwCTQXce5pP+lj7ZQ/MeFhXYNiZq7MP0dIhewoiptgUn1PHPpIo6Yt5wN3e2s3zZFtJMQhzDO4YOlsEnL02puA2ckp4pBxBNEkJCQII2xOAiBBECKqsRgKRKKlzoEakihcUqWKCiJElJVWhPxLhAZNQmo5OsU86gMuichf8mWJKRcu/SD+LrC2FECNYU4agk4DN7oDt2xJopqZPqb0ffzCHnxYe6kkbxQCho0G1o8/ojSOPNkU6l8TPM+zXUk0N+c68ju8dGEZAUw55jmmh9vwUnndQm6r4Pff+cF1GvAWEGyzqsqlMYypcpwSKF2IYbpepbzbp3iHiLSheVHH8l7X/MyTj715YyPj6O+pl2WfVRq8zaNvOHO4kzNTRl8hXEOCZFu6LFtYoZXnv2/ePqH/gYpSlxLiPUsGjyOiEczL0Wzm1uFsYIxSZtGswpfFM1C2eERuKwES0lSEdYIdVZaN5IQpmY4UfyNNiJVEWfbSfNUkyK7FaVQS1ClbQZMd4NNoEqN2NRryehnzX3KmBm4idSqpO5grV30FxgbP+GlR0QI2lgOD1h/VgyFKrW6VFH28NX77+YnWyZYNz1LGcapYgLG2DKrsSt9mn4SAMrF0+ZLE5nzI6UvyyKZ2GRATVr1CUmpCSgwOGcoNFflsZDbWGpSyNfTQMRgCzBWByGkCJYEsW9G8IbaQ3TtTPt3BBMRdWmZaqQMmi80C8TYnaZXs1hYk1rfc/9u+/eezrHrevzReIg0N3ETtAx0Vmz/M8397WywkEWZmpu+UQqzNH7Ej/3+g8VjUF+Yu3gE0kJUWpfrEylHF4GeREZipMJTR8fNE9O859bN3Ld9FjsTKNstnG9z8AV/xsvP/h0e3vgA7ZYQqgpj7Q6rmpFH2lZCShHqUGFcSa+eQS089OCDPP/M13HwH11IMTQPVyQzdjKJk2ZTCnkRkIDRbC8RFQ0+MxXynJ2zkz8SrJZqdUahFQ2RQMgC2ik9t6jJKFEjCTuiyZ+GmPT8CJZSS6gdEh0uGjRETEyiQRotorlYG92cfy3EW9Q7rE+RidBOHBszN/Z/8sYuIFHzDtQcLunZRs8xmIoiOm7btonh4WHu2TLJ5KwwVaeVF6NQO2LyZURM7tdrIyKUvlij6aZIbhsmKViTqPZp8idfjmhCYmWSRXGNpi84C3cHEwniISR2rdpkP0GIiakbkuFUo0XS+JoASRDGJJVtoym1aAp/Tgx1SIuaK9KKr6pErfsRSP98SP8G95Jb4FnkKB3TyAGk10jjFcMjU6OGchN2ihGahSMS+ufdeahqzqvT9fXlEnMKFlDqHKn5x4hBFA+a0rxHuz7ViFHJYsGgOZKT/NoaSxTHJ+66n/feNMMDEyWVU2akorBjVB3L/CUHU5/+en7v/A8w2/PYVpLx07mYjp1WuOb5OlQ4W9LrzuCKNrN1jze88/2sfNu5jCxdRbSKuGGcTfLgYkGswZrEy4qZaq5GqGPIXcMC50sIQlCDSJr/MUZQl6UugdztiFFQFbxVLGV/TjV1kEJbxGazyBtQ4nplMBxzfr+8UCenEwvRU2TGr2oyphJNa7oht2wRxDXk1DpPjMZR8skdu6QHMmCexgbhlWohCDa0UJ1m/5El3D21mSXDowwVwtKhLrUBqQNCL4n7SEy0e9jhw6k26BtSPtv3jVFsGBSEJKQdNcZI9KmWQRTUB/C5wu/BxNRCg4zaCy5/Fvq+rSYj9WxMkye9tkTCHPlDlbSIBJsU27NKmfcpiEy7eEGjmKZ5YQuk84LQMg5jPR3rwHZx1lK49P7Nv0hCtgI5JB3wPSxN+Ns810Qg+ZopUPG4Ii/O+fhmNMxhow2Ss1m4JMGjsQQbKJrJLEnmoMmtjRQgg01EsuJbMwpjURspnFAALSd0CkPLRtpW6bgZLr9vK9dtdAyZHgs6FeMULDAFGrbSChGvD3PwYcewbc1z+L3/87tUW7eDbRRw87x7jFFYoerNUrQLrrvyKs486yyGTz2TpSOLqeM0LkZ8d2tKGdRA3UJjQRUlt2bbSdYybyJOhSh1QnM2iE4PtnagJdYbbCWYUGKCowjtVLiMBfQKNAhDNRSa7E9sLKnxOOngMLRiCdZhWkkcqQhDib5Pil5MDBQm6bXaECjVURWpFYxLmA+xBusChRWcJqR4EHAhtZVd0dkhWn0yxy6Ya+fHJs7NveWQd+VgArU3TEjFw9uFeUPgY2DDZMG+Cz3EIYKtaO0UWj9Ci8ekqnQUh7NQx8ENL9mhq9lRBdlhv0xkvKawmy6y3x7eYcMcZPup+Gb6cobQ6DoMRlMV3/lYoenGQNDQf/9si5Ujq/TaKqY6jSfDjS1oBeoCxmgSoBGhDg1LeXDtOgdZ2WixNtfYaF5GItFk5mdj3DUnTUzgqnTe5JPTFHRzFJJtEpN3Wvq8tv/ezWMuaWc1/h0nZioSB1K3IgKhVnrOcN/2Wb5y/xQ/3N4luDYj0mLWC9Z0MdERYhLHphxlYstm9jzxRfz7zTfwzne9i9UHP51XnX1mv+7h3IDCD0nBLAHJPN/6zjX86fvPZ9seB3DIq87HLBwlzHRx7WHwNS62+r0HsglTIQUaK4IGVIuEn9CEv0CSF3PEYiUSjKeQGqcZqCaSivHapKcFiqFlAlglkFT1i4zQRhWVGhPT4iQhW3pIgVLR6NFYWyAhOSSWDpK9g+B8QIOhsHlehQox2eBNFJf9isSmjVh15lGjtidj7CKQLCPc8qNVm1Sgs2O4jY6ZqseLVq9mS7fHA71JNvmksF2TQrAQY3/iNav9IByOfY1RmXMD7XQh/WN21nzooxPj4IaLTc6ZRwNtfrSRdu6f9R3s+J79OoYdaJc4aT7f4NigiguBWQUJNS6CzV7BNuMDogtItBkjkaOEORe08/cxVw5SVTHepbpGsP0OQYP0bYYxpq8C1z/PTte6cx2neVWf/Rp3tLFoGKPN529Y1T2puPS29bzv5gl+ONnDScGwV0JtaNuICW2stbjCJAHgqR7j8xcz+fAmFu55AC848fnst/++vOqss7n22mtxhU0OferT5iFw7933cN5553HI057GLT+5md1334MFRz8fHW5hamF8fJzYS+ljYTxOPUUsKI1AbGFjjwJDITGpheEpJKU1Bo9IoLBKIVDGmNIaE0ECzsRcO/MYjVj1iFZpoYmpY4OE1ArWhDOqJjYzveVBtj9wH1Mb7qG7cQMzW9cjeBwhcb5ilTssgSQQLqhYCoFOOy1WIjl1J6VUTgzOWJzzaTMKARsNovWc3zA+xuMTH7sEJGvgl43TVS7mJ20DFXpFYM/h+WysH+a01UuozEKqEAke2sZSxZrhVp5lRvE5JUqTPFepSW1VwRPFZo+YwUhBQuzXTuaOnTECCQZMf9FpnnvEZ8vn1ThncZHBQpc6GOYRN11/tdHEqAwmRwdRKDFE6SUbA4QwW/PuOx/GV453HzjGuKspJNUNqqYLoqCFYjw0cOwcIhAl1Tlcwkam1ExDupE0EI2jZRODSCUgaohBsUVq9fWixwrYbFokYmkynMazNrUsE6S7EEOtBjun60JTm5L0/jGrvCmpBSrGYvX/Mffm4ZJdZb3/Z01776o6U/c5PWVON0lIhyGEkMFAgogCVxxAMIABgcggiCSAcvFywyDCjRe8AkIAMUQMiuIPAbkgXAImaCQTATJC5qGT9HjGqtrDGn5/vLvqnE6CQmh5XM8DJ119uqr2tNa7vu938AxTQ6Esn75tnq/urMRYOHaIlBJknnnKvpH2qw8o1UiWcNHF1g3kOfm2I7jiliv53de/kWOOOprzzz+fN7zhDWzevJmJiQmGwyE7duxgfn6e3/qt3+Kqq66kyHv82nOeR+/Qg+hMzOLDkKGvmcxz6qYkGIVqEloPSamDUTV6vCUT7GM08Y48SPYzvbIWpUI7gaa2I7bq7QvyMJuUSEZLEp3K8aGRvOaF3RhfYZTBWmlrW+3xVWJ57w6KqVmc6xFNwqRaYkswJBVREXw1kEq504PQ5se0gLIxBlLAJ1BaJiLNKARrNB7cvv0ptnEBeRDHq+Pqh3utscmTvOGOpUX+6b5l6pUBw+Fejp+b4YkbNxOoccq1HRLG2xGFedBnrK7ea4lkD/c7D/8V/6NG6MP8m4d9cc2qHff/vHGLtP2sgLThpJQVqXVtDEWK6FRw93LFeTftZdCAjoHX/WCRP9ys2L6pgw+eXClWbCIP8nAzZv0KdB1TxCmDpjXdWdMJcSZQpkQea5L3FFkOSBeBQib9FKHnpGsQU41Vdj+tSdKjFq5cWdN64UsWnHyWXPFRa1HOh2l9O6Re1BSAJ5DhuOjuJS5fjmzqbiD4PpVtQIuzV99ndDOoq5KqKYmNx+/byXBhD42umJ49goMP3c53PvcJik6Hgw46iA984ANEEnt27ea6G67HKcNhRxzOYYccis0cYnut8T4ynXrU5T5yY7B5l0G5hFUaEw0YEf5LX00RtfRIUpIO2ki96tuHExi/loKSqEtiq7Kl9QNpLQZSIJo2RyAqlBZSWp5blvcu4AKgNb4NgbJtKqGKARMiw8U9uLkMZRxJaXSoSVqjkyXpkrpahqToaIsuNC4Zojak0KzSLAIkr2UCU+ohi+yBGj/2BCLMjHbLgV5dfBXEtBqIpLSFumJTJ6O06yjponSiDmU7IxrZH6uGlNRDugqjlV/s31rByX+BsR9nYrTaqFYhHBNRQx4DtdaooMhUIEX4yzt28amdS2RNwhU9SB67UPG6pUWevW/Ay7ZN0MkcRdAkLY+vR5LPpMPTGjgnaSmKwpKWM6KJWvHJT17Mvfk6dKzpZjlZrilihsLTURD8gEZHuj6nay3dTiLvTuJ9ic1yMmTblVlH7QPagcLRyR3O5VRVn339vgDEtZeENDTLw5IQGuEqpEgTG4Z14Ct7h6haEYyh0ZCMJWujEIiw0UFuOoTYkGWarJOhtmxkcMgGlhbuZ9fu+1leXGDnA/cBq5L96D0bNmzgqaefAbRbSCe3cgsvUw2X8VnDlBIgUZdDssySfEIrSEnRJE2hICot5ECkC2OsIiXdijzFPDtp1XZNNElL+1622SP6AKAMKYqWxcRE0IaWRkRmLP2VZWK1TKZqPAU6SjyIV2m1UWAMLnniYBk7M4eLEHRBjB5flTRLe1vph6VanKfrLEGDTYI7BQUdpSW8RgmJTxahAw+gwiOYQDRqtdYf/WwPvkOgshZTVxw12eO7ExP4uuF7D/R5w+O3UPuKzPTEUHaU86qEOTrabyulZNEfSRxp81xaYPKnPR6MlGh5mseTh3QxxNZQWrGaUtWAI7cV95Tw3uvmub1s6HuFCtM4tUIoM0KhKRcGfFF3+Nf+bt6+dZat6ybQ1KiRqXSy6NEEqgCMgG4uoUM7wcYEjeaYo7eyW83SmerSJaMOFTE5vFXguuhUEoNhGU/fOXxVEeqGlKQibPyQsupTLZV0bEf4PlExHC6j9D6U67J+ejODMBRCVFRYk9HbXBBI5HpCTIStRyfF8T4w17PEGtAOVfZR1gkOhjh2RW0otCUa8T5dDjUL/ZJrP/+PnOJq3nneW3n6n+wieI9WCW0c1rYdNbPKXB7jXgQqD4uDJaY++h6uveU+tvz269hw/InoSlPYnMZEMgxZVEKyih6t2/ccYTpKZPTOGWKUIHlhkFoxYk6i69Kh7Sa230P8SSUwyuBBW9m+hoZ60Ccj4ZPD2kT0HqV7qJQwyuN1Q5YUZWhohgO6M2Jq5HQCrVhZXsZQyaOXQOsSP1yhO7GZGD1GSxfGp4g20j520bQV1H+Oz8SPX4HE2JJtVqmxUcu+PaKljFKwpAfcMT/PkZOWrRvXsxRqpnMHQY95F9oIUUu6FIKDhBTRQaxk9KjrEFq0eQ0e8dMaQe0PLKaRWhJARay2xKDQ1qMCKO0pcChVcf1SxTuvWyQGTY7hIBvRnT5BTZAmPHnIOXzGUTV9dKV56w1L/NKWFc7eNgnKECLkxhJiQBlFjFLhuQyCH7XTLSrTkCqe+OjH8NcX/y2nPOPZeA15VWCyRBEToVkmtfT7XEdoGiG1FQqVAkkHvOoyHS06rRdpuBbiVBpZzyuhUM2kgjhimSoFybbJfQ3eB7JaojA6aAZDAe9iUwlI6yUiocKjjcYBg1CigiHvTfDAvkXKXTuZ/9f/y5svuYzJDVvY2y9bwabGR+H9pCCCE4PeD3fQ2pBbzWGHHM7rX/9annTSSbzyNa9j76FHsXnLQTS1RHp4FJEG4wImJHxlMQ6pMGJDo8CqRAj1KsbV4k/G2rZqDmANUSuyKJ3I1YWlJoQEKSc1Q1Sq0T628Q8NTQA3tZ7JyWmidxBKVpYW8c0iXWvFB5VIZjNIDTElejPrWNwbsMFDGIK3pOEKdD3kilR7gkk4kwhGk9di2pRUxMdmddE/gOPH54EoEb6RVr+LSjJDNkQKKeawlWLbpim2dNYxFRt2N2Ubeg22nSy0BHCwdnY0yhC0dB5AahNtWqblT3HyGB2bSavkLXndElSAJH6bISairaXdRiB6x2II/MOdA9594yIrQaNNxDpNig6jC7CQ1YZ+M4+PDVmWy4qlI//4QMl5Nw7YWYFPmjo0JGVIQTxUtHLUrZO9RCDIauRxFBOTbNM1lTe4oAkPajTFAAAgAElEQVSuxngj+3Al+SS5QijPOEJKaO1AKxQZOlpMmCCmHB26wmpsukTVxYeM2HTRYQqPxUUrpj24ljSvCNGKWCxrUFlCOY9FHOoKrbG6ZYriMUoT42quSzI5sVqhqjO+9Vd/xsUXfoKZdT0u+MhHsC7n7//uM9zyg5uxiBGPMYbo9/f51EqCw5q65MwXnMU73vYH1AvLvPd//RG7PvQemp33tQxQYR9bq7HBkpJBF0IatNaDU5JAZw1KC7NaK+EHuVTI8WowRn7q0C6iStihQnK3JBtRqgYDsY4YGwXw1ppuPkG3N4n2lmQTJi/oTU+TpQIfGwqtGZatyMFriAaVLFPrN+BVEjKihZAEr7JJk9mEQxODFj5IEKq8flAX70COHx9ETYkxbN+2cVeZc56ohKPvTYdNztHVHU6ZdfQmc8nOiBqcWWXdefFYGL2HHtGK2+lS44VW81PGQBKreId+mJ2T0YZaBTGHxpJMSahybi4HnH/9PIMUqRvFhA7UUZOnBFZR+YrbPvdJ+rfdwuNfdi56YhNVGqIoyND0lebqpT4vu3IPzz10lhceuZHYBIyD2CLuyrTU5tbMSClFbjVV8Lzgmc/izy79Gkef9HRUz4J3JN2g22uWdGAkVzfS20a3aX6GBp2NugaGJnoyp1HRY41CqUgKteS6JoNVBqscKQ7QKsOa2Kbap9a1LsnWK3lCS9ATzDZRx4BGt05ZgcI47hssMNizh9PWT3HyyU+hX67w6le/mjPPPJM777iNCz7yMYbDIRdccAELCwt8/OMfZ9euXQwGA7JMoN6iKLjhphu545bbuPCTFzEzN0tVNbznLefwqg98gGec+x4GTWQ6N5RRoYPGBCFaeNUIOSw2kr9MIGtTDWFMXkdFhdGWoBpIDkuESoOWTkjyjZhlZaC1QuuMyAomCZZiiRTdHklnFLYHqsQGBVlBXhTEUoyyXPCQPMlGAVOJZMqRFwpb5vjYoEMkU0kwm1o23MGJrYQ0myVG9sEZOQdq/PgTiJasFhTjvdjIaNmqjMbX+Fixa1hzw75l7tv7AGXd5+RDDuKU2Um8RvwNUmidnVp6+IivMf6gtUCL7El/2hjIw00c0OoRiGTKYGholIVkuHZxiT+6aZECg1Y5ha4psUznUpJqBZMq4zHPP4dbPnch3/rAO3jsK85ldtNR2FwxKIUEVMQGV0zy9/csEVPGK7d1GCSF1cJR8bEhUQidXGt8S+YDOHLbEfCZfyQ6hyoH0HO4mONjIlOGqCIxeSwOkxIqynYjGrEziEG39i4CIKqQoawX4WHyKJ21VoRKck9yQ5G6wm9RxRjgy1Kg0uIWF9BkqW3XG00THU45FqsBDqm0+uUSdy1FHrjte1zwlj+gqiuauqRXTDA7O8GGuScxt3ED37z0Mj7zmc+wd+9evPecfPLJYyykaRo2btzIzz/jFzjmqKNxuZDc8txwwgnHs32wwGD+fqpNE9RNYugbZiY6kAqSX8Ykqa6VV5JsHxFvjTUUCg04JPhMJ4VKggWhV7k4Whswwn5OMeGSYugraKNDIprM5pIz5Ido10ozktDVk5VJVpp+ElxFCESlUM5iU5eQ+uioCM4TG4/VBVFrbOuTSjAY2ybWKfMQUuSBGo9IjUvb5BspPkfDq0Zyb21Ophv2NHDIZEGuZ1heGuI3TEITMZkR2znkRI0czTS0kvqGqCxqDdX6pz15KGTbZNL+OIh4t1pZWVIJOFz0XLkw5CM/6DOZacIwYVzAO81EBB+G7cpU0HcRM1zg8F/8Zebvu4edF/8NvTe/iV7YgJ0ucYuB2naJSTHhNN/cucxKGPLbh67HWMEwtNUkGnwULgjGyKpoDFUTOfXIjewZ7MMU66jqAVmIZE6iGVVVSeq89USfsLok4cBXeAmEJQXBGRSArtDRkwpHFsR4GK/INAyaHB8DnSRtadqOUVAVMVlcnWHz2ILMmpgy8qBxRsiEw2DpZg5lFDvrJQaq4eiiYMOWWZoyUte1XIvW02NhYYF3v/vdnHbaaTztaU9jw4YNnHTSSYQQmJmZYWZmhgQMyyHOWKqqIniPcZYqNZz/p+/g7De+nclnvYzOSSeTdw3RO1L0hKSxSqN8IFqFqiNo0I0irQ2pUkKBTyShsSuxIhQAPY0dBFSQlEWncoKXLb4AsggojgfdRZtAwMk9ryFZcFhAo5u2qRA8xmhiDOMOqEnggeRHlZ3wPYJK6CahjCd6h2s5VnGEIR7gbswj44GokejHtAWCGLdobfEqEEKgZzIePeXYs6hYqko2TGQQ270roVWz0kqR9YOqjxFV+j9z2/Lg/opsWcbttFadmlRYk9cLRrf5N6mkIrG777ng1n3cMfDgGhpfkOUNylhMCGgLKWSEukHZgRgFqATesP2FL+OWz17EyrU303vipLRGM0PhI6GlOONyrpv3nLu4k5/dPMEvb54QVSaSiKa1JbSGACkKue9pP/8szv/K/2Ni+4movljdNQliq/qUjGmPshIhEHSN1QYVGiKKTAdU0PiYMFocsPbccBPdWLFSLtLNchb37KP3M0+jWhTtS0jSWfFRU5NQqsGrPmFehJZG53gTqUJksNIn4aiaBUzpiSsrDIYLdPIJujdeTzV8Jv2VAetm50BBCJr+8iJf+NznOeecc9i9ezeXXnopmzdvZu/evTzvec9bpQ8Azlh27NhBjJGJiQk2btzIdKdDcfAx/N5rXs4PbrmLO/7ham5bqTn49F+lc/ChZNHTGEeWZyKE9J6QIl4HiVloHb1C9EQH2kdUpokBjDMkD7qN+kgqjv9nQiRqTa4tZRwKHmVaBmmCEJMEp5mE1omQJK3AAFp7dEpEZaQr5xJaZTilqRCGqk5GfEr0SFYheUsKg9Y12nfFU7X1KjnQIOojdGUf9dzb2VYJSw4lVvMZGdct3S88kZgotCVTDeOJwouwZRVJGUVjylBKoaNgRxYOaBt3xNnwKuxHDBP8RVSzo5ayb4lbIQm1PSrxgwixRmvFLQtD/ui6Prv6iZn1FWbgMG5I4zt0i0QMJYpp+uU+Fr5/HfnCncRhgokuanITk499PPn0BsrhTvbu2svGuVmiUYToMXmBoRFPEkp86nLRHYt8b0/FWx67CWwki0LFUzHhdYNJjpgiU9MdTp3I+f43v85iuchKXbPsE4tVlDydTg/bm6DSim5vmjrUWJeTuwzb7aCtIaaMyg8gZRRdR33bTXzw7F9H+Yqkc77w1S/xlzfcRhEsK0u3YJJjsNInpAR1RSj7hKois444rNDaiOShawn7FulfdQnnnvvbHLn5UB71mEPZNPcEdDHJnqc9jrNe9FL+9IN/xlwr4V8aLGOV4vrrr+eggw7irLPO4vLLL2dxcZHrr7+e5z73ueP4y5EvRowRdKAu2+5eCKAarrriSn7/Tb8vDySec1//Bi7d7Tnhha9l7qjDCc0KPsvIraNrYKke4pwjaT9eOK2PKCNyQm+FKW0NkuvS5hkZpckawGlUiBRZh6ocgG6kSos1WhfYaAhe41ppgDwlsnZqY0T8p5UEUQVIbkgg4PHk2uKVANXJBLQSVz+ntcRVYwTfokb950Agj4TKvtYHo51CRluQSmOtYbkacuKm9Ry3qeKfbtzH7uU+UefEVKN1TsJikAfTqLXuGzLEF0S19PWAjqIhOBDll24xm7VVxVoewcikR2GxqiaonDwTjkLXpJbPlvi7+5b5hx8sYLpdNrjATFjPYq8i810esLtY2adZuW8Hm676Gr9w4lae/JQn8Phjf4VIomlKbrzxRr72rS9x47VXcPjGKX6WPXz/q/dxVxW5d+PhbHvqrxGyQOG6TPbmmF9cZmOWc1dT84ff2cXrHjfDwVaYkVY56hRRVmHIIDl+5ZeegyeRo0QqbmDkZFEOVxguDplf2svC3gVhzAYYDJcpBytUwwFF0UPbCh96LMzv47q9dzE9uY6kIs5aqtoyPZhnYHOOPvwxxAaKvEenmMJMG6yZRE9OsLzcYLLAus4Et37369z15f/LzxyU80dXX8E9997PX33yk+i8w+Fbj0Npy8YtHT520cd55/94F0869Um85KzfZM8DO9kwO4f3nvn5eYqioK5rer0eN910E02z2s1pmoBzGXNzcwDkeYeVlRWKouB3XnsuZ7/itzC2kFZsaPjAhz7O9Td8jws/8SE+/9EdbP3Fl7DtZ8+gHyLzsaFTdKiVxSkx8s5jQ201Koj1IGiUj5K2h8GkQEwenSRRz8YoQfFO/FccirIJqCaQOhqjM7RuMMFSmyDnVkliASqinZg9NyESiRjlSGFIxzhCrIlNhrIKFS2xqWWhMyLH63g1ph0If0u2MGuT/H5ShuojqEC8VCDtg9R66As2oAM+Njhj8THylZv38rQjNrB3pQMpkhMkeMrG8YGtPZjUgrPSHIhor/FoDBGv9A8BNX/M799GUiQVxy5oIxMdGLVsTVs65lgdaYLoYFai4a6VkgtuX2DXSqTKHZ0mkVnDTtVnyiuaMGBivqS64hLecuqj+Ln3vBW0ouh1GJHAY13z2Mc+lk7XcfDMek488SQ2b5ql28uJSnPnD27li1/7CjfXDbs2bUcfeSwzFhrfJ4uT7NU1b7x6Jy86osd/27AOrAiFBNusMCZnua6wRhGaBm0L8O2l0mCzCbINOQdtnuWQ1uPEq4CLworMtcPXfZJyLOzbw4c+fiGnnHIKA18LRyEEfuG049n9+a/w/UNPYGL9oeTWtozZRPAlKg2pd+9j4da7WfruJWQ9yy8cfTQ//643s2luA433fPXr3+B3Xvcq3v/Bj3HCYx7HzNwsmsT6iRnOf9/5XHvN1bzrXe+i6DruuO1utm3bxvXXX0/TyLZsx44daK259NJLefrTn85wOKQoinHGblmWPPDAHVx66aVUVcXb3vY2DjpoM0Llb2noxvD444/nT953AXv37uIr3/g61//9h7h1UDH9pCcz99inUQ33kaHJ8x5Ra1yUzpK2FhsSyQioqnTAJ7G5VEryd/FgrAJyOt1pysE8SkVWBotM9rooZwmpRqGxQSj4Lil8THTTSE0uebigqYb7hKbe2jbmKmEzR0iBzLV5Skm2Uo3TWJ9QRiaxNmxilXR3ACCCH5+JqtsKpMUuRk+dKDEDzmaUakAWHHcMGxZv3cHifJ9fOnaGRhWomAFDSDmS4qXH5BvRxABEdLQk7cUXwSgRlh2AoZUjIO3GkQWc/FkqKps0sf1eiQYXHJUSaf13dg547117Wa4SXVXQ0SUxBkyyzNmCvt/Dzm9dzpHXfpULPv4XKCJFkZFURqxKyAvRlhSO2ET2LVQ84THHSfSg7RJTRmZg27GP43eP2o72C/z5R/6CCy+/lMf95jkYZWhsJZqNJvJXty3zvV3w8u3rWOcUTlV44wg0dLVGoVDGolSU/TmKgCWZRI8cn4RRaY3FNBFsjk3QVAN03sMMlvgBPXaccSbqqi/yHBuItoNWQ0iBm9Yfwoatx9DRTiagZFCmgO4Ed13+z+y46E94xUtewG+cf550M5ICH6mrJYKvuOPm67n2u1vZeMjh/PEH/4Q/PO+daG0xRuFcwVOecgZPfvLpqATDqqQoCq6++mouvvhidu3axfr16/ngBz/IF7/4RZ71rGdx//33o5Ri69atzM7OEqPnmGOO5TWveQ0HH3wwKQW8h0hFnnWAhhQMMQlYu2HTIbz4+WfBmR6U5q8+/mHe+6pf4dgXv4RHn/oshr6kl2doH0h2lPkiFDqVPEpbimRotMcY6ZxI5SfPx9TkDDR9quQxTUO9vJvOZEayGSrVqEzjdKS2mowuzoLTIoVAKfr9FViZB9VIVVEbTGHQQRFbRW6BJpgo1qHNgKgdOhjatF0ORBW/dqj0CFRnowlktJW57c7b+cpXvsJBhxyCMpqqqvj+zgf44+/ex+kHT3I0OVumDTOZI+qcEComig4TM9McfvAhTK1fx/q5DUxPdUlYsQXwWgxmHaQILugf6pL1445Rqy2mBqOFxi1OZq2NnfIU2tKEkmAyVB254O69XLJzhYwuVVyhsB1iqphyU1S6Yd/KElx/Paffczm/9553kylZkVWEJmqcjW1tOfJ1FcWxAq688kpOOumk8dn1VYnNuiRVo7zmC1/+PP/9gx/n5Ld9mNxO0sREnRocNSlOUjDg5Y+b4fETU1gSnkRTNnz9m5fwsS99jbkNG9jSm2I276BNYHIqZ1p3yDtw0PpNlNUKed6h2+3SySbJc4svh9w8KPncvZ7h5Dqa+d28YGKJX3zWs9m58zZe9urz2PDrL6Lxiv7unVht8KHG1IEbvvE58rt28PSX/io5XQZNyYrusFxVMLmBYB359ByVU1S9ScKwYff/fitXfedKmiZQFNk4yX7Uoh1tUcZ6mDXZuKNrOnp9NJRpIy7V6u8I61kc1SVSdWyJzKgV7kMjRCxrUUnzx2//n3zuzl08/bw/pty9SKfXQ/sG5TJ0qAg46XJEobqnJC7uITTQZh5F31CXfaIvKeshro6yMKpMwNGYicNePYQklhDaKpQ1+BiwNfhQo5yVSNDGElVNb90mbLeLUjmuPdQmeYztUqYFbNPjrmu/yZ899yn0ZtaxdgI5EFuZR9SFGblmjn6O1Jk4h4oNSSueOHs4x033OcpNsKQXWZrPOH5jhg7NalgOLc+oZXWOUGLVZs6KObBMUg8R2/2EI6VIRGMVIpuOmmQiOiQSnhUshY4UNHzktgX+efciE3kOscLaApMC2uVU9RKZctiBY+aWb/DmP/0/YjSDyKpRBmcjKVhGTavVm1o8XY8//oRxTZeSwuYTMqmRg1E8+9nP59jtj+eXX/qbnPG+v6UyMIEiJEdHC4j9/u8NeN2jAyfPddB08FnDaT9zMhdfs4O5Z7+QWlfcEQPUnrKuIdSsLO0l7Q64NE0IibrZQywfwMcKZwqqlMSb4r4dGBP4q/tWOPX4m3jFOz/K5pe+iqV+xfRUl5lDjiYGLw8+gUOf8Qxi0QMTqKuMwmb0Ys16n4h1SXSWEDwLwTLlKm791J9z7htfyWWXXcYZZ5xB0zQ458Y+Ed57rJXKdyyp16t/NxrWWvHEaLUyq3fo6oQjbdS2G6HM6utIqpxCIXOUxfsh1mSc+z/fyuO/8U3OPvNXOPvCv0ZVJbbTkX+jRM9lxnKDKDKuIPd2V2U0qaHWUK7sxuqsbUsr4cxoSGUgpFL0OEkRtMfSIcQaVYFTSSrd1l/FxPaetRlZR4HuYrQnNLG195TqOvmIUYkQR4Hl+1cfB2Ir84hyYdJ4qzF6rW0hBSmZCSVlHlF5wyAMWdwZOPFRIi13NiOmUhSOKaDHE8Pag4vCA0mrIv4DOX2MT5wWr05x5mrFUiYyBFQT+NZy5B/v3s2tJRiToRskfzQ25KZL39d0dUaz1OeBP/s9PvHxD0EbfaiTWc1pRbw3R+r/UQU3ik4cGcKMeAaJ1vCndTUjJR512GF89sN/wtu+/AUmnvZLYrRLIviK0kVmbOATt3humg/87CGBWZNhix7HTVfsWN5HRg1uHTQ1hdEoldGZO0gsIBUYJRWeU6JoNSnRxAadNF5nkIZYk/Oar3+bR73qDygHfSa2gDIFoW4o8t6YC9E0FVQDKm2xapFARjDCYFDWEOqKIiswwTNYmOfZB63jeb92Jn9w3ts444wzWr5EGwWRAtbaVni5Wk2MxmiyGA3XZg6tXmv5uVqtiJ5coUitiO6h7y3v6axYIqQQOP300/nG336c3/n91/OEt7wfOxygp7vEFY3SQyFrtR0gByjlMVSEpNEmYUpxMNMJXKhlS6mjGEcZMRlSKRBJWB3RlMTYYFUuz0nb+m0zGrA2pzs1jTUZEEQFrBO0/iQ5SdrPxNaUIY0rrwM5HsG7yenf/13kzyklKTWNpRx6zn3Udn5u2yaecewW7l2Sh7NsJONDKNSaODKNXeMsploDIcmDMWKX+CM3sH/47ynAp0TTlqom6vHxqNYqsQmK0MD5t+zm/B/s4M6BJSqNb1p3cRwqKiodcQRo4Nvvfysfeceb2bBly1h3EFtm4ZjqH2NLIJKHVSGA5tgEhjUYUFr1nFWocaL9A/fcz1HlvfiFRRrlMUmTCoeuDf1MNBtX7K556/fmuWmlxtqMV//yM5m/4TLIJsi6jnyiwBU9TG8KJqbQRY7tddBFRnIZQRf4LCflBfRyTG+Gbm4IeUZ0hultT8R7z0Rvmrw7S1EU9GYmcXkBucFmGaY3QWdiHVkvo8gnsS6nqzIsGqsVNssIKbCvv5Pqzpt43W+cSWeyw+bNB7Fz506pEtoHfhwholbvsYcbD3am+2FjrYv7SIH74BVY4Ve9pxJk+QTWwrbDt/GON53DFRe+l8ZYwjDhcskjMjq28Q2t1ki3aYttPKuyRvROyWCMgmSJbVaL5Bw5krIk44jBEKOV+yQpSA6lDDG4Vt3bZd3sHFlvWqrU5FFKy/1kIla1x2k0iiDCQzjgkwccKEQlru6lRtlySUHf9qnKhj6ex81OQEByNmgkTEkJMWttKRUZ+YDI1iXRQPzRtzBBPQhtVasck9H/m1Y/Ij6uDd0g27DcRArn+eBde7htUZGljNw2TBCZzHw7iSk6tpAKgy4r/SFPsENOOPHJQt5ROSBt5xD257b8KKdb8mDbSXW0dweUzviZnzude267g6v+8sNY1cMrTagbptevozsIdIxC2yFTJP7u9hW+t3OejYcextwd36fXK1heXqQplyGUpGqIHgxIsUJVQ/AVLpToNKCIQ2LVx9Xgh0t435ANFLZq6NiKzCbqUFMNFjGNxg8G1OUQygHB99FNn7rpY3xNiCWJusUbZEXEN9RaMfCe/q03U2zaQFMnXvGqV3LJJZeMGaggW48Y5RqKeFBOStMIv3yEjaye459sRAIkAZ7TGvzEmQy04pRTTyN999+YmZpivipJvkKnglArqDOilz5q8k5Ik3W7OY2eFBt8GuCVhFiB6Jo0ilg3EBuUL1ExEmNJ9AofKpQZ4utGJgMFnbkpks1R1Cik+6NoIDWkIDrjmCpcdCSrUa0l1H8Gl/sRWRrGUWgRrV2/Wl0ZDIqKQNIGEwJ1cnTb3rhS7eq85oYIIY11MJIvIyI1seiTPrvVYiz7o7RxLY5RXg3AyEB4NJweWb5FSauLGc4Eoo985f4+/7xzkd2VodEDbGowoQdWIgKDHgVCV6ByrDHsu+4y3vnGN8knjDJaovhfjtqJq6FID7VaXLuySgWyapMwonBLAepBZfz5x/6CCz76Yb761+/h2LNej1eWpl7E5mIpqVSHqKTM/fsdNVfNL3HqU07hX+++E7VuE9oGII4fZiHPqdaFS76DjzXaOomoyCRdnkLhQ0KT4VMkqhrtDOWwjypMa2uY4X3EKOn3eJ/ASEJtpRoheHmNs4aFpUWyiUmO6zr+8bNf4NnP/WXWT6+TiT0EyrLEWsv9999PnufMzc2htWZlZQVr7bhdm+f5GCM5EGMUuSFWmaMLBbQrPEReeNavccsPbqS75WCMUnibBORXwuCV5yISmrbKjgmrciYm14uxm4mkKECpG4GsLfYnC1vEKXk/05I2UQaVwDhLSgEHVAFMUsQktpRKi/YntViRRkNLenwkDn0/yniETNQRlV14G2rNd/NJWp46BYIW96eglSQOEfEpkRs3njSEBr/670NrLhTGeIEcvEWPRXv/3oixdUVv++HCT5F0GTm5IjDKFaz4xFQrjPvfP9jJpbsDWzJQPqCJFCpDGVEnKZ2w0ROt5O6Ww4ps/SzsuJMTT3uxLFRK3ssYg/c1SyvLrDPmh97cDw59kvPbRjXENN4ayltrlBbF6+/+zrn8yu3f59yPfoRjX/Y6lndH8gmDb6uewjiaGHHGs2NBsXjo8ez4t2+xYbJHVOJILkqx1rtTa0mSS1pEVyYnxSA6m6BIKol/hZKSOCZIQVOHQE9Zhk0jK2kLSloL0WuiSbgUCVFjlIeYo1SgaiLLRBa+/U1+50Uv4G//4kLSs59JdI5nPvOZvOtd7+KFL3whn/vc59i2bRspJe6++26OOOIIAHbu3MkznvEMjj32WMpS2rtj4PUnHCOSYRo3ChirHkYFydlnn81L3ncRWze9EN1VdIPCpwaNFfOmVoRnrXjZVD7hehm6WC++pkom5ZgQCUFKBLywudtMGWXEZSxTjkY3qGBQ1lJXA7TKRS4ABGUwWhNCI5i9FooCWuPVUConVbcH8V9AC6OUaFmE3mXaqAX5u5QSVmUEhtJVCeL9UUXf2sMZbNLUMZKlkSv4KtiolMIlqBRtNCAYAlFpdPzRZlCjRIZto8ariFWqlY0nTFKyhzQDEgUTKjFfD/nEzctcv+Dp2oBOXYKpmFKOfooY5cfVQ3Dg2siDJiU6YcChUzmEEkwOREl415HoFX998ad4xdmvxEzY/Qg8DzdWK5C2raYfgjSRGXlAkm448ohDOCavmd/ZJ+9adJKOjdWaJjY4DJUpUB3NTN2wqymZzh26jFR5xFLQxAqnnWSqYsRiNRhSU6Nd1vqXRKK2+FBinMHSxZtFbJNhHAzrWiqNAKUuiUC/bsBn1FmNCRqParcFNU3TMKyXsd2Cieuv49BXvZRff+0ruPjCv+blr3011113Hfv27eNf/uVfOOecc5iammI4HNLpdEQcFwLD4ZALL7yQo446ar/J40AwK8WqsA3/GovBI7S4FTGgdJfplIQi7p1YUOqEVp4sGrxtJNs2SjVmnNzfVoWR4IoUa5TVmBTBtOFqSBFbhUAn5TRawFGXEmiPSgFtDT41WGVpQiIbNSMM+CT7Aq9qCt3DK0VMYiewWt0e2PHI1LhKt1q3Vbn9WJY/2qrQekZq004wo767ZNcqAyNbennfNqVNjdLaRsrX1LZzf8Tvp0QtLD7hAaUcITZ4q9AWuQhYgorcvrvkT+/dx/zAM52m0G5ICiWZFsA0J0mObJT9sNZG0t+rpCAAACAASURBVNeVJnlPP8ARE5Mi36Y9Rg0pJgZlXwRm+Y8G/45vfNWue6undvzeo5VQHDgbnnvm83nJ+e/nl859B8rXrVYoYMlITlGYhuATyU0QSCxdczlRRZqiR7Z+HaUWk6OGSDKaGAwmd/ikUVbTVH2CztBJs9jsonfV1WSNYrFeRlvFwhN/njJBf+f9xKoi1SWhGuCpmOwHOgR8VPRiYk4PKLRnbno927o9Dlk3w+Ne83KiNhyy5TCuufG76IsuYmZmho9+9KOAVEd1XZPnOQsLC5KVmxJFUXD22Wfz3ve+lzPOOIPt27dTFIVQx3/SrUwL3EOrLU6wNpiLVjIwYx1JickPRKx2+Ni0E78RLYtWhBiwGBjFKuiWh2Qd4AVzifKagOmJ3ERp68Yo3sKjjx459CklE8/o+VGCE2XWQIxtOJkkJAKEOMIB/wtUIDJWgb7RGO2xtNaEWrJrrbX4pgGjhbHXQhNa6/2i/EbvuXb1WJusENWYMf8fjnGEA6MJDZS2bUC3RBMMSfzDXfN8YceAzDimjKPWJR0vVoRRtxNG1GijZXIjkWoJEIqNolt0GJQl660WGeboGNrG9Nvf/nZe85rXPPKy+kGzjmAsIyJUQtmMxzxqO4+av4V9e25ndm6OPBZ4I9aCRhvRbqQM6obtp55OtThPco5q0CcsB5wvSSxjk6W2kTQYiGCwieS5o/EDdOoQbY3ZOc8f/tqzefyjjyXkls/805f58uc/w+t+6yV0HvcYOsaRuVaRTWLDho24boYlQ/guYoUZgoRux5AEDwgarQ2/98bf58wX/Qaf+btPMz8/z8zMDE3TkGUZn/rUp3j605/OzMwMSimapmFycpI3velN3HTTTVx++eU453jqU5/6yM71wwxJR2zp6GuHdhAHbOwoFq3GYYlh1Gput+RKo3TbZYtieixb0iQNAaPQSqpUlEaTWvxCLnRMRnxUU+uIx5rMHy3B70kp8RtJo91Aa/epxbRI6xyjBzjMOO3wv4QaFxiDe6t/bn8qCVHWbUIXCBuQKo5PgCglV9t0Y3BHr4EP0yrfZPRB5seoQiKSY9IK8gGFi+LTfFc15IIbd7C7mmIhNmxIitoaUnBgxMlJJyAYYQKixHbRS2UB4DKRYi/tnefI2fWyP17Dakwp8Y1vfIP3ve99YwD1kYy1Rch4Pm0rv5gCWWZ58Vkv4mOXfInZM1+OMppeqii1gpgwQWFMQ+x0cbUmW5/jI3R760ibM2KqKZLwP5SxqODbSTyQJ8cw99gQuOaiv+DkjRnrpjqQB0yoObRrOahnmevOccSxh7f72PYKqiQksBQYMTy1lkBuYwz4BmF6a4wR0PQ95/8vTjn1JO655x5OO+00AC677DIOO+ww+v0+c3NzYsIcIyEEskzK8u3bt3Pcccdx+eWX470/IDgIwPz8POvXr3/oXygIPnHwps3cvWsHUxsOJc8lOtJpg1KWJjZYDJX2ZGToUBOVkjxoBQpJjktK49AkbVDetyukIZgajUYriVPVesQB0W3zAYyFGNL4ejnTJtPFNr5VBZQSBndHeWkrHODJAx5BPSPpWPvrZ9diIFprrF01vS1QTPcm2tftmEUoUc5BLBDbER4k6QfEsTqIK/b480f7xYf8vgILRiWMjhS6wWlNx3oy3XDvoOS9311gX+phXeDwjqNjNBbZv+rUyGpBQhsveRxBQSPJe8qI+EonET3de/UVPObkJxAQqfeIB1OWAzZt2iKemS1+8u1vf5v77pN4guXlZXbv3v3vchra/3jI6yEGLr3smwJkp8Spp57Cyq234Y2kkEXrsGhM1OROoVNOVpYELV4cmgalYcI1mBRweAqjmEhDXIJCS96IVYpejHRjh595yWvJthzOYYduQfIn4ZQTTuLUE5/AEUcdBtT7I+kJaPVD+70eo9hatv4UEoIkvAxjDG8451yuvvpq6rqmrmuqquJDH/oQL37xi8fdrL/5m78Z30NKKay1aK2ZnZ19yET94MTCH3WklLj00kv3Y7muOQjQHY5+9DHsu/EmsA4/KOVvGo+vG5IPRJ9Qw4Bvt5YximOfbiyusWQqJ1MKjcO1ZlBaLKKxqcDYgtx2yW2OoYOKDpWsaGC0JUUt0Zeu3bYZjSLHONESRRSNcQQbQWXoTP+n9HEfIQay/2sjAHTUmk0pUdYV37n6Gnp5B51ZDj74YACa2PqIpiDep8qP95xKjxrD7WSUICpLMtIeA1qMpdVDKPbbBKXUqlJJZFG2KjZF+jFx2a4VvnDfgCERl0T45VOUeAkkbFopBT5inSOVgWQUOviW4ackr6N1Cyk6FrfjVrYe+WK0KqTLo6Rtd//9u9m27XDWSqe/9KUv8cpXvpKUEldeeSX79u3jOc95Dt57iqLYj+Pww1q9MnnDpz/9adZNFxy7fTtTU9Ps3buLmcwxaEoKVQhojWTvOJWIWhNSomMj13z9q8SiizOTBNXHocmVRTVB3MVzQ+M9eWYlfqHJmOz1WGgs7/vLv+P1L3kht91+C6949et5wYtfypcvvZRBGSAzNClRx4ZBf5mUHMu+IZBDrjHZJLUv2b1Uckyc57df8iI5vhjRRjOzfpqVlRWOPfZYrr32Wk4++WSuueYa3vrWt471LTfddBMHH3wwWZbtV9kpJdGVV155Jaeddtp++o4f1jr/9wBXpRSLi4tcccUV42po/O/ROB3YesTh9P/m87ikxPMjillySh4dJW7DKScLS/RY7UhNQ1KKpBMqlVS1x8RBWx0kVJQuJCFSqTiuNqwSjDDQjKkNwDinRrUdy5CixDtYBaagbob4ZFle3PXvPNE/2XiEWxjamnp1MhmBQDFK8NH83n3s3LsHyoYd99/HyaeewhFHHEHHFVRBZvYIkAxJedkjJotWiUa1FPME0DqCjS46ZtyiNahxaT++IVpgqdINWcy5sd/n/bfuQ9WJIQZHm0qvDKbJBTRraeMkTbIJqohzIrLDyBcxMdHo1pcUw0p/hdO3dISGrkfbM7n5skxTlqur14iWfd111/GUpzyF5eVlbr75ZkBK5TvvvJMTTjhhXJY/eNIYjRACdV1LJ6KsaUJkeaVkbm4TMYmTeEoSvWiUoDFGaaxr0LWR9mGI7DrySOy6QwmmTxparGnwKuB9RmoCLnlM4/FKeByJLosbtnBtP3DV//lzrmoU0+/+BB+aL1Exx3WW8D6jMZB3Cnx3QKF61DrRjYEyRlwy9Ad7CVQ8rhgSgsNaPYbSTjzhSWzaspkjjjiC8847j+OPP56tW7eysLDAxo0bqaqKG264gec///kA+1UbS0tLzM7Ocs011zzkvP1HPx9uxBg57LDDuO+++x4i2lMIP6PT6aD37Wkns0RMg1bdZcXTNLW/rSEpgzaJpjYUNjK/ew9NM08kkJselW8Y80S1UOyVdggpwI2jLEbfJRAwSVPFKOr46Pe7971POOdJZUmTNKms9ysED+R4ZJDsuGOg9iuLRqxAnWDLli3M791HWZZs27aN4VB8QYe+pMgyuZlTQI9yE5RMJCklTEtvD6tuAbJvTIilfYtFSMUifh5aKVyr+Vcq0VGWq/at8N4bhpRDyaV1SVzGMmxr/xfEsTpKOLZJER1GFykJ0o2WFqc2ZFYisIexYu/N1/HsM35WPDhYbbkmIrNzm7nttlvwMeBryUPdsmULn/3sZ6mqiqOPPprZ2Vk+8IEPcNFFF3Hvvfdy8cUX78e2fHD5vbi4yKc//Wl+/7+/mWOPeTTVyjz9Ycm3r7mKLSec2Jb1ogTV7bfWJHSqISrxYImKY55wEsM77qDp9/H7SowfwErElZA1QzpqiDMeOprkHEYrnEpMY3CTNTue9KtsevLPoXYtsL4D612DCZqus0yjyOoFemXE9BfI+ov4ppLg8GEflpbZddk/ceoJj8foMJ74E7B161Z23HM3ACeccAIrKyvjyfWSSy4hz3N6vd743FSVCMRuvvlmPvzhD9M0DSsrK/udswePtXT3/2hLs23bNm655ZYxK3b1971YCKbAEx5zlIRvqVqO0Uh0htyDI1lCS7aMBmc1S4u7qMISxnZIFISYhOauxawqRtkOx9iQQkLjha2NKMZDbAQPTEF8VFM9ruCFtdugUfgUiNLyIax1hT7A4xEyUeOaKgRYw3TTWlN52RrkeU7PZDywaydPPvooacGZnCrUdFVX1Kljgo74qgoTL6Bb7odqBWYxSVtMR4XXNZZcykWMKBQ1WO0wytMAV+1a4VN37sGagilyGrGgpaMzEg1GWRIVCkvUCd06Jztykg44pQgGMhS+xXaUStjMkGqLv/07PPGsN5JlhRRiSrUFmabbcfSXB+N2XYyemalp8jxn7969bN++nZWVFY4//niyLHsIk/LB5bVSil6vx/Oe9zxWlpb5t6v+jf/21NPZ9cAOPv3//ROPfss7CVVNpkQF3OhIiomUapLJRSRnhKy0+eCDmLjuKkgKb8QuIZpAFh11iFjdoUkNmbbo1nfC4iEENBlKB5KFomeJ2mBDYKpwRD9EW41KHWpbktGR/Bc6mAR7Y81Eb5ojFm/nyK3HolIt4q4WkN+6dStf+9rXOO64x3L44Ydzzz330Ov1+Pa3v82b3vQmAGZnZ9mzZw8bN24kz3MuvvhipqamOOecc1BKcffddz/s+Rut3A9m/f6w8621ZsuWLfuBsuN/i8VToa3m2KOP4vbBIhN5F5QlGo2Onqg8Slk0pvVTlefD0xDLuiU6JqxLWCwq1nhtWnWuIxDQyYlXi3LC/FYjFoeWOJHUdmG0BIMJOpuhYyBo0GSgS5QFp3MxjvivAKKOZ+K17QFWeSCji9Hv95mammJ6dj3OiDAIoAp+taOwBiNY26oafY60b/XY6Dil1H5j2fet1cfomBikyF1LDW//3j4uuqtPHTJy1fn/mXvzaEvK8t7/8w5Vtfc+Y5+e6BnoBpqpGWQQBERGMYCoiGKiUcCrUTEqJkoiJpiQYG4iUTHmau4V4lWj3vgzBEcQAS+ojDIo0sxNN/R0+sx776p6h98fT9U+p5vWcBHX8l2LdbqbM+xT+623nuf7fAc6MUeriFWQ1Ig2jqgMVgcyJETIGoOtvC5BcmkVAasjRlWJfEWJnW7zyrXLSJIq5LrW31Q4rlKGk055BY8/+og4gFnL2gP254/+6I+Ynp6mLEuOOOKI3u9aJ8DXFdyuwrH6GmVZxgUXvZ1/+qd/5tP/9Cn+13U/Yu0lH8IUlv5W3yyI7SOpVqLyVFqYvWVFWy9zlrf6KJOISRLIQQeD91Gcv/EYJe+DUUr8Y33AVnkyNgZMV9FKMigC1qTE0mFNg6gM6IgKkiertUbbSKFy+mdm2HH/T7j0/DeiKMDUqb+yBgYGIESstbzkJS/hwQcfpNPpoJRi06ZNAGzevLlHJrv55ps59thjOeuss0iShDRN2XPPPbnvvvt6k5re3tjFN2TX67trO1PjK2mazra3ve0uTE+jLQes3osNd94Kyku4tgokCtLKyzRSSFRELFA4Zia2C1PXixGyCZX7urKkXmQYSdAkUWFVxZ4mEH0gegjOEw1V3q4AgFZpmVZqyYBBS8h3qsAa+Z7Cdt3d3fybrxdUgez6WuoXJ09cQygCIwsWcNxxxzHY38+9993H5q1bGBwewlbu1gJcInTtsMsTYKeRbay0GrNjTeF31LGK8vdCKX45OcWnH5xkPOT0ZRk2WkI5RZa0wBSQR2KP0CZah142r5KwJQGJJWEsWk2K79n2x9Ai8RM8etetXHLeKQQlOfSx1gpX1RLA+973Pm666SZWrtyTZrPJ2rVrhTZuTA8UzLKs93Sc29PvrgKpvy5JDEmSceOtt3PkQYcyqEdItUeHqoqJEZ1ElMvRyqKUJ0bQ0QrO1DKsXroXDz36S+atXC2xmKHEmkgZkuohINyBIkDDVnoLnZIqKLqBRiOh6ybIGn3QKbGtJq4701O3Wl0pkJUjRgPtDq5hWfLgnbz2Ty6EigOi5zwVY4y9aV6z2eT888/niiuu4Nxzz+XLX/4yH/rQhxgfH2fp0qW9aVcNzIt0wHH22WdzySWXsP/++/fo7scff/xOOMbu5AO7rvr9mcu9mF0apTyQsPfeezP9z5/HvuLVKO9pmJQ8dLAqoLOUGNrEAFpHiAWukHbOa0V0kQRLa3iQru9CAKs1XmlUDFibgvIQGtRhYioqvC7lgRNFlyXMtESc973HaknJM8bQ7x0xBEbnzfu1mM9vsl74FGYOSaF+440xRCeAT7fb5Vvf+TbHHfVShgcHGal0DHLzB4wSwlE97o21LSJIcHO0FSVL+AxAdSIbKedMJFUOId9pvv7sJD98bDtJo5/5TqO8INaZSYi+IHqJIowEMmPFLkDLSe+jmvXfiMJhjVFBKHE9yTeMtSdZsmAx5plfsHz5m9FGQTRoBc4FHnlsPXuuXEWz2QTg6aefnjW3qQ4BYKenmtaaG264gRNPPFEA2V+zwa+99lpe//rX02ikbHrqCY5buA+tZoYKke7MdLXRA7GEYKQE9hUnxySGqAvwhtaqvel+7zpmlu2FDZboNM4YLIpAQcSTqoQienIfUXhcKeCxSRQ7XEGq+ihmHGmrDze1g6gbKAd9DUunM0MjBecjnTynnBxnZsMzfPjoQzG2+ZyUtPpa7NixA5g1EHrf+97HVVddxW233cb999/P6tWryfOcbrdLu90mSeTAq0V11koWzPHHH8/y5cv56le/yvT0NKeccspuD+hfNYmpx8O7ew8AdBXfqlEs6Wvh8kmiNhSxSQgKggXtscaishKDoZvnaCzaCMM19zmDe8ynlVgSP6/HFJ2twnWdJIHSYpSkUSgjoeSZUhRRanCtFWUIaGOrwQSgLEUsyEKCVslOTm0v5nqBjmSqqiIqgKjaACEEjJXN8ej69TRNwoYNGxgdHeWgdQczb948+XpbVRAI6QXogU/1+NbriAoS4kRtN6gMaE9GJEc4Io9NOL64YYot7WlC2sDGgNUBpbUcDNjKO0Neua4oJcpIBeKCl76xkvgbK4JAg6gZgw8905m+RLN901OcfMDK3tMWBb50fOP/+wabN2/moosuAuDhhx/mnHPO2an83fVJWIOjH/vYxzjxxBN387Sb/bobb7yRa665hjee9wbuve9+TGIZGpmPMqAs2Lw+ABGlgdLoGFBI2auVJwZNVAVJ0mDdkuVs+Pnt2Ea/0POzlJxI0IqIZgZLYgwzM11sVEwoxV4z21mYOTp5l6w1xL0zisIobITo2pgutNPIzNQ0mU2Yak+xaGQ+/XnB0rFnefMVf/bcG1fJflJKsW3bNoqi6MVUDg4O8ud//ueUZcm9997L7bffzuc//3lGR0f5wAc+IKzW6jCundkXLBD3du89b3vb2/j617/ODTfcwGmnndb72bviGr9qTU1N7fZzfBRLSh8Dp59wDN/65S856CVHM1m0SW1AOQHyQ9TisYuXltuXcr+4wEB/i2aSErWVSrxqg6NWlKVouMrgSFQqnrlVsHl0iqDFckDHQFBSURuroXQ9g3DlEwxUbSzP4W69WOsFHiCy6hK0tjSUU04wi73XrOGpp59maGCQjc9sYvPmzYyMjEgNMYepCtUTIdYYR1VCO4UjSPYGEas0OZFGhK4OpM5y+44uX3p8iraP2OgYavRhfEEnBetStCrRGhpBEZQEMUVdoFVKcJJOb0012UkM0ZWowpBGi08rxl9QKA/KKKZDYPsv7uIP3nLe7MWIcNPNP2RibJyVK1f2NtxPf/pTLr744p2vm1I7lcUhBO655x7uuecePvWpT/GBD3wAYCeOQ33ofOELX2DTpk1s276FO+64gxUrVkiPjiEJvpcn4rXCqEiiAgVVbqoCoscYsUuIZckrzn0jfmIM7z0znWm8gkbUFEWXvBuJlLgiEnQkMQqlpjhu3xWcftLJEBX33P0jRloLGWpotk055s/rp9FK6Ovrg0bCV/7HNWzYOMmFb3kLC5YthZgAs27ou1tjY2O0222UUr0xurUS2H3UUUcxPj7OEUccwaJFi+h0JK+l5tGUZclf/uVfcuGFF2KtZf78+UxMTHDKKadw55138tGPfpTXv/71HHroob0Kp77Ou7KF66f1+Ph4789zmdM1nmdNylHHHc+1V/4j5tjjiTPTEnuhG3IghICyQaInlUSG6qjRRrRKJliJilYap4VGEKInNWJBkWlDjA6jNTHKzeqMYHYocRiLUQLdo4+gNUFJtrRJPaZISaJCac9va72gMW4tq6+p1fXflZKQo6hndX+PPvooqU1YunSpoNrGCJGmJ1iS5WuhTFDoKKexTUAHRdMYjIYBrfE6kHr43FOj/OtjYySqQYKh1ZxH0W1TaE2z0GgDplYLEzDaiVQgprhYiH+miXglPh/KSWmqqqtigkZFL5OhNKCtIfElrZlR+vsWEVwOaFxZ8uCDD3LEEUcwPj7e2/jbt2+n1WrtxmJP9Q4PYwxr165lZGSEww47jDzPe/9eL+dktP26170O5xwj8xfyxBOP8YpXvJzoRbFchJLEFhjraCRVBWYCRoEx4mJljMF6ScUzNuLKEjdvBLVgD0aW7svy5WsY2XMvVux9AHseuJbVBx7KfoetY/8jj6M5OMT4E8+KlwoOcAykLf7vj2+hi2Hfg/dl/srF9C3YA5oDQEo2OI+pTk7HK6gCr6La/eFRV2lnn3023//+9ynLEuecjNN9Zc8QAkceeSRf/OIX6Xa7NJtN2u127yC49tprueiii1i9ejWrVq2i2+32/jvuuOP4yEc+wv33389nPvOZXqtTYyf1IbLrqnGnXdvKSBA8iUASYBjxL9GNjESlOFUSg5HIyahIrCUGRwgFKjoCM6QqweuCRFmUdtReMhZTudfJGFgrcbOjGndbDUEbjJKUQaMsNlGkWvX2fAyBGDXoEmLEBV3hlC9+FfKCpjB1xVGj6DWI2ivPZeaKKwrmDQ3R39/fI5rlZVnlWlRfW5vn1Mi4QoA3ogQ9E3EOiugp6dKMkf/51DgPPtumYTKaSaA/lcDuLMuwSOBPjF5Gw6qDTjTgMMrjkdFkVI7aQCfRiiToqmWCEKKAWirFJBqjIBhHESJ7DDRlzJwkQMAkCZue2sDq/fahKIpe7zwxMbHbzde78NXNsXTpUh5//HFOOumkHupfX+e5X3Puuefy5JNP0mpmTE1Mc/Th6/CxJCu74p1iEylzlScaEVlZjfAUQkB7aU0coFVKDAqbe/qDWPF18zZ5x9HG452iEwJtV1BozbK9V7PXsiUcfND++CgH4soDDiIZGmZo8VIRE4YGVMojlOGwIw7nhz++gxyNr37fue3u3KWU6GGOOeYY7rvvvp6q1jnX+y9N056Yrm79Wq0WeZ7z7//+76xZs4ZVq1b15BL1z8myjCRJmJyc5GUvexljY2OCjc0xIqoPk105ImVZMjMz85yRL0jrq4BgW+y9cB6jT29AdSdROpLoDGNn93esfsck7UPpBolqVXvT4FxX7ikVpZWOBamxktOrIlbJn62yvetnFRS+U9ldCPjvrUbriE09SSI2ASaK7MLE+t78HbA0FAfr+uLM3vRze9tQsTtPPP1UXnLM0SxYsICJiQmUUjRsQuwlVWtCpWCsNRE21oxTUNaRxtkYyrvGZvjr+7dxz9YONm1gjMdHT0NDoksKJQZGynpSLze9VSkmgFEpBEuqGpiAZGV4hS4VyhliVMLaNIokBWsNJOKIrV2g32v6BloM5R15YpD0cOTle67iycefYuv2bXJdlGJycvI5125XPKTetLviIvV1nJyc5N577yWEQFkKIU2bhHa3w8EHH8nWJx/GZgmh7Iphk9KYYNDBY7UAjKnKMDqQWKHZZ2WKqhzTlPbMFJ48n8Fag485upuDLUkrAFrTplQKfcQr+I+fPIjqzrB9fJyvf+krnHjcCbSaA7hOF5QndnJiXjLTGWdoeD59zQaTO7bRbk/KVqve492RubTWFEXB2Wefzcc+9jEefvjhXnVgre21LG9/+9t58MEHGRoa4qGHHuLzn/88IyMjrFmzpndoG2MYHBxkwYIFPa3MzMwM//Zv/8aJJ57I1772tR5BzDlHlmW9SgdmWa79/f3MzMzs9FqVUvggWTreC4/mrFf9Hv/2iY+T9g+jlSWoALE6BLTGGkOz1c/CJUtZtHIZ81buwdDwiNzcxlQKW8GBMsRXBifVBMHLUEF5rIJMNyBGGogocWJ8ByaWVUsjFbPXcjCmWjAtrYPwQH4L6zeistcg6k6OYnXJWT0lQjdnn/327Y3eCleSJOKihAq9sexcTCQYT4Kh8IqOCZR54OqHn2XDtCHaiNGWbixoakcaU/IyoGykaST+UoWISyPaV0bIIRBdIFW6IlhFjLISBlQZxxhL5QAmhjKEgNKBJKbkmUYXJWNbn+HIxUsop3Oy/qb8/gpe85rXccEFb+XCCy/s9dONRkN+l93kl8xtY+a2M/UGrr+HtZbHH3+co446apYIFWHjxo0ccuQRTH31P2i8MmE6lmShH689MmBMhVbtoxgyBY03qgLjJKYzBboxZ/1Pf8A377gTqw3TIZA0+mgODOITyEoPSPtDfz+xCPznXffy+NPPMLVkOX/x2DcIrqR0U0z5Ppg/TGNwGOcDcarN2JoTuOHyK7n3iss4eG0DrRs7beNdq7NWq8Whhx7KoYceyl/8xV/wmte8hqOPPpo8z2k2RfHaarU44ogjuOOOO7juuuu4+OKLKYqCbrfL8uXLe1aIte3hkiVLGB0d5dprr+U973kP8+bNo91uc+mll/Ke97yHvfbaayfgFmYPkJGRkedML2TvyP6X/tixx/IVrBvKmXziKeavXC7AsAHvI9oHojWAKI8N1ZQGjTYFVieUZY7SctDFIGN3jK9SEkNPlR6IaN+lLKYYHeviyzZliAwODmITVbGPgagw0dOJGqM1RgnGuJO8+0VaL+gAqa3dhEU36gAAIABJREFUegTuimmnq7lTWUqY8tZNzzLSP8jm8c0sWLBAyi9rKYMj6/lOxp1IaFpDf7R4Ik3tsVHzP9ZvZXOZgekwREawFXErWsoQMKkwSUPtBKIMulRED0nt55GI+lcpRdDgvEeVlqzhKKMX2XTdipHIBCYaSFuEWFC0Io9+7zouPPck0v4+YlQUZUkjMaxctYIkSznkkEN6h0GWZZRl2TtInnMNq+tVX5O5a25lUj8pa2+MSGR8xxjBRRY2m/hEEwtwLYchRUeFCzlap2jlcVEOgKBm2ZhWpShlsaHkuONO4Ktbp5jeex/mJynaOVRiaUaD1w7vFM5qXABjLPeUDrVOEx14E4naoZ1lyPree9KfKoptO0iHNUu++xD77rsvqPQ57Wp9HeQ9U73WIsbIlVdeySc+8YkeEFrf5MYYut0ut9xyC5deeil9fX1MTk7SarV48sknUUqxfPlygF7lVreWixcvBiT64e///u/54Ac/2Duk6nazxluMMb3vXb8+WdXp0Pu3hNIXvOkd7+drP7mNY1e9gRgVqtnCTneI2iPloQUfcAgB0phILKuQMZMIWcxEYrVfUZFUmyrQHVTw0HFM5JN0ZibkvsGTaIs2RkDUqMTyQym8DtL61JR2AnU27ou5Xvh32w2zra4ydGIpXEmr0WB4wQj5nPDjXt6HF25/8DKGqr8+xMBk9JQ+cuOzXS7/2XYezxXR5zS0weuKXh0TYf/pqg2JChO0tCVBTlxjqmyZRNSRyojVfeo1VgfSrJAbOMiFqDeQUVEYqVGhQofE5ZRjm+DBO1h3yGHkZUnZaeOKEhS0Zzp0ZtqsXLmyB5qeeeaZ3HXXXc9xDZ/botT/trsZfYyRLVu2cPzxx5Pnee/zy7IUIVcKa+Y1mXhqIySpYBxO/DcMSvg1UWPkF0NVfbDR4jNhYqBhm5iBPg6ipOETUZQqBc73bjwVIxQO4z0xl1bFFV3KUFDmHXSuiMET8pLgBVAvCk2nPUF7/Xr+5PxzaFQYx68C8XYdldaYyMUXX0ySJFx22WXccssttNttiqLgc5/7HKeddhoTExN0u10GBgZot9s9VfPY2NhO9PXbbruNd77znb3rWl/zv/u7v2NycpKrrrqKDRs2CN18joR/eHiY9evXP3fj9/A7UdoapTl0371JNz3O1MYn8DOTqDxCUok9fYBYUovdaqavZMNEvAKdWKJKqpteODllLAg+pzM1yfizmxnbvgk3MyECPZ+iSFA0Knwx6ZlO1XH1McjDMYmzjnkv9npBIGrdwijk49xYB0G0I1YbWq0Wm595FpzvoeouQIjiAE7UaBOqGz4QoiEATWX5wmNb+eITE3S8J6lu8ASxkTMO8EF6eQeprtLHA1WOhkFnGp04VMOQRk+iDDqArvxu0mgJHiGVGUNqkyrTQzaXMprMinDPT23lW3/8Nv7nJ/9BrPlLhYvCh+i2c375y1+y9oD9e5WELx2nnHYqd997D1rtnFeyO/LSXACv9zlas2LFCpYsWVIxUMXz87HHHuOQww4lBjjrrN/j//z1n9KyqZTMsbZFkHIYrUi0IdWGzFgsCk1JojxKOxpKYcvAuoULKLMOZVk9gVUgGPGw9Qpi75pYdDBYbUgqr5OAx5ci6FI+YLynU+TYiQ57PXEfb3jT+WidkdqE3RW8uwOZa0sI5xwnnngi8+fPZ3R0lHe/+9185CMfYfPmzQwPDzM8PMzk5CSdTofBwUEGBwcBmJ6eptvt9sDRycnJWQ7SnH0aY+SVr3wl73rXu/ibv/kbrrnmml6IFcDChQt7NPrZ1xgFmI5UAreUEHNmpke59F1v45o//yCqM42zBUXRxiQaKt9fEQ6KY76Q/CopQ5TqWCPWEcEW5HmHsS3PMvb0RjpjWwm+EOey0MQWDTINGI9WEuOgtVgxKi3iz6ACVllMZfE5N2LkxVwv6Eiam1dSU8l73AYVUCFirGLhkj0YHBykf3CAVquF2L3FSk2o0Ijno9W+qgC6jMfANQ89w0MzsChqHJbCKFJrQclhEmMk2OrmT6UlQUWskRQwa4L4hkSN8k7m4hGhq8eITgRzSZS0sTUHxSJek/1pi+gD3RjY8NjPuf3P3sm//+8v0D8yjGoXFNNttClpaUMndLj9tls49eRTwBoiUiWYJKPotGcp99VTYS6VHWD79u2UZdl76kZq2nxFcU9sxWyVf3/08cdYd8A6ApF99zuAFXaS6fEJCSyyldlT5ZFptYEQxbE7SA5J0MI01UYqlWlfsHL//VEPPkCpA92yTeFKfNmhW3SgCPiQU0ZPnndA5RgN3aLABUXRkZCPbtGhLD3TUWHbT9Pu5By3bIHgBcCvqj52uykr86k0TQkhMDg4yDnnnMMVV1zBWWedxYIFCxgeHuYnP/kJV199Nf/wD/8AQKvVYsmSJWitSRKJ1ZicnNytkG5u69hqtfjsZz9Ls9nkggsu4PHHHwek1anZsfXyUUSbPpRExKTH6JSoDB08/+2NZ/OLR+8nzSNJkqG8opGkmCgYhYqQRIXRglQpEnSlVTHKUnS7tLdvY3r7FlQhB0pNNAvRoEyXEB0+5BhnyGwisosgjmZyQIFkK8lgIKogYVa/hfWiaGHqqYzkuSp0onniiSfZ+ORTHH7oYQwMDTI9PY3WFhO9ZLxo0aA4F8hMC2MMT+aKbz+6hW0ukgSDbziMk/l2UB7tZSTlNFhvCLqLNhleFZKvgcOr2Ev0UjFgTXX6Y1FRfBfwAgCbNMMFoQcHL5oFa1IJP7ZNCt9hxdp1PPvyV/P+P34fy/eYz8OTBZ/4k/ezzxGHkA32U27dzp2338GHP3oarpvjYsLY5HYGB0ZoNAfISye6huqsrh3Ea7D57rvv5p//+Z856KCD2Lp1K0uXLmemM41G0enkHHDAWt7+9rdTeofVhqef2sjLTzqB4IDU8brzXsN3fvg9Dv+9s2i0+oheUPhEJRgUTot3RIxSeicqw6uIjQh+4lJWzltKtmE9+qCX084D2hh8gJhpuiGSqJQpZ0i0ZxxP2Z4hKzM6aZf+/gW0x7aQJIZ8poNS06jpBB55kPn7L4Zoq5K/ykt5HpqMuVWaMYb999+fW265heOPP55jjz2WDRs28LWvfY2NGzfylre8hWuvvbZHZ280Guyxxx6UZcm2bdu48cYbWbduXS83dy54DbPUgxAC5513HieffDKf+MQneOqppzj11FN7Y94QAs1mE6M0pS9IdCqGS9FS+C7NxFKoyLmvfjUXf/ZLjB04Q3N+gzJ32CRDVzYLseJzKCUcpCxaygqMndi2jXxmAptolFegvSjfEbcyowNl4dFW02gNkbYGaaYZwVpE7VHxSZSMcB1Qm+f8zkxhelqYOavHC4lRXKY97LFoMY8/+hjf+853ccGz3/77snDhYnQiEGURczwtlFVMdUvu3jTKnd0mkGFVidcFxotlvjEWGxQkkagCqdZidRhSNAUGiXDQiOeH2M6KFsFqiEHJuVxpCjKtUEFUiygxV0wTQ/ABaJNmFsIMiU6weYeXX/BOwhvfRKkMqyJc/ZUv8IoNW9lSzvDWM17JAz//GcNDfYTgaBc7eg7dRdGlr9moWmbxg63Zk/XT7/jjj2fr1q1477n00ktxwdPpzKCiZmTeEDbJcK4grWwBHn/8Ud78h3+AITI9M80+e+/HF2/5Jq30XEIshe1YOlQaiUEEXoSSqBJQAR8dmcoIKpAVBTaJlA3DW085hQ3bHiLLMmYmu2hrKDttsqyJ8Qn0w7Ktmzhq31Vg4ZHH7qe5x2JM+1la8/qYmRxlaNECotHMH5rHD7c32G/PvWWDqKrffZ5V9K5t3ktf+lI+97nPceCBB6KU4qSTTuKOO+5gbGyMG264gQMPPJDR0VGGhobo6+uTcXWWMTY2xvr163nTm97Ua0135eb09nB1sMybN48rr7ySmZkZrrvuOiYnJ7n55ps55ZRTKMuSqCC1Kd6XGGNBBxpJi22dklYzQWcNhjoTwBRx0tNo9e9Eufd4tLaSC1N0GWtP4IspCpdjtO5JOrBANKJyDm3QCc20iU0S7MBA1YK5Wa1W5YjmoiPVCaUWPxj5fiLQ+22sFzaF2eUQmVsWmpjgVIex0VEIgYmJCQbmDWF1JqUlKQUOlBB+xorATRu20RlYQbQOZ8RzQpcapxw6M+hYggHjBTfJYyDDoKhIXiqSRIWPgcSkeO3IKkm5UQmlDuKtgHiaFrFLkqZ4SqxPULEt2gIiWqUUrkvLtFBZIIQmRackocl4GRhMUpaefwET/cPcecs3WfXDn/DYk0+QtIYo8i46SXGUPLH+UZJEFDWqBxbtXDqHEGg0Gvz+7/8+N910Ex//+Md55397B9/6zvVcdMHbK3zBYZOU6elJut0uI8PzmJmZ4t+/+21OPe0UdLct11V7nFakOsOlJdaB0xobHXEOs9WYjBAKUlJiYulLErqu4MBjTuMY7Qlag5UYy6ASTNJExZxtG55g8dJhfu/Uk4DAD67/NmsPOphle66AGuVHQzCgO2z65S/YY/FS+aG17OV5Vh/1zVaPU40xnH/++Vx33XUcd9xxNJtN9tprL77yla/wxje+kWOOOYapqamqTaaHY3zuc5/jQx/6EAMDAzt9v191iNQiurIs6evr47zzzuO1r30tN998Mx/96EdZvHgxK5ev4OzXni0VdAziNqYCjaZMWRIdOfKg1dxz083sdfBhFP1DNPv7yJp9KMDGhJnpSYrOJN3OjAD/WsuNKPFyROUwISWPEQOkfYvo7+/H9jVwToPO0ZXYMcNQxBwDOKVIYkoZS5q0aBuPCQYVKlzntzDGfUGmyrrSAtRM1BogqnvNGCPtdptHH13PdHuKbVu2srUyEQ4xkinFgLZsy0tu3biDjoaWyWll0G8CtgreNkZhnUF5oQSjrXgnAMoHSpzEPVSAoVG6x/pTiElzCAG8GC1HCnws0V4TfInyjhA9KraIzkiocaFIVJOyDOTTDpU7jA2UIWdhA5LuJPNSRWd6ipcefTqf/da3Wb7vYfziwXtQJpLowI3f+SGXf/oz9DX7UQTKvKg8KgTr6Ha7vWtZliX3338/3//+93nZy17G1Vdfzco9V3HzzTejdL3pA/39/fzl5Zdx2kknk9Llf33pK+ii5N77H2b/Y49FZwoVS0JoQ6mE2agklqBWehIUePGU9aWlcI7JfBKVKOhOUdiUoAWs9a0hVCsRu8lY8MBN32X1ioUVAFay34EH893vfptYeMCJCzuCdkeaTI3PkA0MVlVHqIYXz68EmftAArnhh4aGmJmZ6RESly5dyvLly7ntttuYmppiaGiIoaEhJiYm2L59O//5n//JqlWrWL169XO+964eH3N/Zn2I1AdOlmWcfvrpnHDCCaxZs4as2eDvrvw4BCUh7V5A6yQdwDRSJtqeI498CT+85l+IqUcbTz49yeT4DqanRpnasZWZic0UnTbaJJg0IWgDQej0KZrSS6U9b2iARUuWMbxwPiFTFC5HxYLClzK5jBHnZZLoCLKfVYmJkWAU0CEQCUlXIjJ/C4K6FzSF6VHYq+OsbmF0VYLpqFm7di2LFi1iv30OoK+vyZ6rVlX4SU00c8x0HB1rpcMLwgK1WpMo8R5NkXGr1ZoULZRsIx4e0Wga2sp4EoX20iKkClIKuZmtjF2MdaBECWkqRFx7cbG2lKDEWtFYwEacK7BZQmrA2hRVdGlmiQCyrQau48lSTe5KTn//5bzqz/6GK/77p8jznJmu4xfrf8m6N7ybn957NyCl8c0338T0dHunKEalFA888ACbNm3irLPO4p577uFVZ/4e27duY2JqkhAjeVS4UPKZqz/JIz5j5f6r+e//+nVecsJJbB2f4Ht3PcABx78cVXqsNlilSbQSBqQSL1moDlWjZwlrRtLMEiOcG9NsEoOjyHN2TBd0t+9gamqKqe4U086w6szX8ov1o+Rllzyk3H733Zxw6ivJMcz4Bl4nRBSlL1DAltEdwrvQ0MsjeR4VyNyDo64S6hv+ggsu4Atf+ELPtf2Vr3wl69at4/rrr+fuu+9mdHSU0dFRbrrpJh5++GE+/OEPA8g4epex+a6M2F1/7lzQtSxLTjrpJBYuXMjE2DivetWZ/OOnruL+u+9l2+hW8rJgaMECTJZi0MwbGeatf/Bafvbdb4qWSRmi75K3ZyjKNjYaEi2M0xgEYI8WbJKR9Q+wcGQxi5bsSf/QCLFhCAFSbySnKEIjJOgISbDSFkfRxNQiPx8DeIdB6As6SJRsrbd5MdcL+o495mnVysydrccYwWjKGDj59NNYtmI5hSspy7y3eevsT680zUKk+kEFdLDVtACh5CqJZKnfzLr0t0QawaBSCfYxKKwxmBjxusRXJkEB4UGoCo0maoLz4iGiZeKiUkvDKrJEkZiIpaSvmRDLNlliIHbANHFlLgekD7T6+9DR0sTQ399gjzV7cdyHruCDl/01U+Nj+KJDHErp9A33FKUjw/P54z/+Y8bHx3vjxdtuu40nnniCpUuX8o1vfINXvepVrFmzhn3X7tejUOM6nH/+m/n2Ezs478N/zRcfe4ZVr30LDz2xlWvuf4SzP/k5YgFplqErqvjcqcPcv2tde62AthCjTBJUKFEEXNey49lHePLOO7j3vv/Lk794kKfXP8amRx7m2cef5f8+8TMeeOAR/vavLmdHe4K7f/EA3/vhrXzv29/gX7/xdT7z1W/y8av+idI7JibG6Ovvl8pDzdkvz2dT7kZw1263CSHw4Q9/mKuvvpq+vj6OPvroHsZhreWyyy7j1ltv5aGHHuKSSy6hLMULdC7LtP7+u3qCzMVd5k7J5nJ3jjjiCI497mVs2Pg0hxy8Dm3F8Hjr5i3oYBgaWMqipQuxSYO3vvl8Hr35RvLtO1DRY61gf6kSsyajoJGkKB3JsibzF46wdNlKhhYvpdEaELNkpbFeY4KIObMK7zBVngyArfQu2sdeOLfVKUEFonMyTFBi8/DbwFF/IyYqFbW6bmGg6i9DJAC20ceea1azcNEinC9m9QZKi9o5NThV0FSphAKbANGAEe6cco5gGhAktEnVpTiA0eiyIGJQMRCRICiQ7FpClf4VQBstc/B65OyFgq9tLTLzqFCNom0V95Amwr5UhhgillSoxDHgOjk6kcQx4yL9fSN0VcKaP/gj/vBPPsIhSxfz1Nf/gwPW7o3WnqgsBx+yjr/6q7/iH//xE3S7BRdeeCGjo6Occ87ZXH/9tznooIOYmppiy5ZnWbFiFQsXL+L6//gPvnP995l39Im86pw30e2W9PetIFGGN1z2FxJ7sbWDSTx57qsbTxErlzdihDp/uDKgJmqC8qRa4YOC6CQDKoJRM/zo1h/zzXIYk5X4OEZqMig9aVIQ9D78769fj08XYjY3KTd38YkmaEM5vo3BomDgJ9/nzz/4XrrdgqGR+cS5IYbPowL5Vau/vx+Qw2BycpKJiQm897zmNa/hK1/5ChdffDF77703X/va1/jTP/3TneIy6gNjrtJ57gE79+NO+7w6VOpxslKKlStX8sgjj/Cy447j8ssv57LLLiPLslkRJBmLFy5nenqc9737Xdxwx20cdeZZtGc6NE1WRaMi0Sa+Htk7Ou0cqwu0sngbSLwik28o4fQhohIwTqADHS1aB3SAoDSqGt2bKGHoGjGVMlWk5m9w6X/tekEYyK7+inMtDeuP3pdyaJQ5xs66cdWU2jJqtBOXsESFivACSpdoKozAZOggiV+WWHE2IgaDpxS9h5UxrzbVk0RLj6sDhCgHlfOIPsBqjJWoBpsIUQptxI9VgdZUhrbySq3yRB0x1mOUxetAtCKNjoUjlm2CixT5DMOJZenIUi78x8+SnvpKvPeMj4lLuEKhjWHFihUcddRR7LPPPlx55ZWcccYZrF//KE9v2sib3/xm9tx7L9LM8swzz7B96xa+dv13WXHO+Zxw9psx2pPoEq3bGJ+jiwLnuyjbIamEc70nafUkqhWYMYrNggSci9epC14O3jk3UH+WsXr1avxAH+nQIuYvWMLgyDyGFy/BjiyjMZwQ1hyG3XNv9MIR1PwhmoMZZrCFbrQgCfzRqS8DIgonSW1zd1j8zX0plFKceeaZXHnlldx8881s2bKF22+/HYA999yTD37wgzSbzZ5Z9dwx7fNJCJyLkeyO5BZjZO3atbTbbd797nfzL//yLz0zo9nvoWj09XPuG34ft30rKu3Hl/Jg1cGjvQHliB5SZUhCwHVztm3fyo4tT5FPTRBDG1/ZZUYvALUvJEkgeii0lwiVEDBB2K5aa3TUpCoRUhkJRgXwWWUo9DuAgcCcknSXJWIvXQXnCDZRE6Rm3wz5/0bJLDwEIHoMKTYaUq2wypIQsHgSG0GVaOOrQwK8KgQTMQVGBfHuKL2whQuIubgwaUClGmsCKoFEOcFWrBDSIKAr1qzgOJqGlV4yCRkmGpKAmN9GT+YTrBfAViuhD4fgiGiM0rhGoCw0y/c5hFe/689oZhlUIUP1IVsUjmOPfSnvf/8lGB348pf/jSNfcjjWptzwve9ywvEn8pN77mLFqpW09juQBav2h0zhnaVhElIj2hqltShuAV/dKD0AUFcVm6l+x9pIWsk0KqhIqhskJiH1CUmwZGQkieGww15C/wO3QpLR0Y5SGXLlCNqIn0VI8EXEeSUu4gS6ec68pmLw/jv4wwvfKtaHtiFhz2ru9Pb5bbdfB7ZqrTnjjDO45JJL2HPPPUmShDPOOIOPf/zjs1qrqnWpLQHguQS+X7V2bWfm/nv9cY899mDevHksWrSIo48+mo0bN87+f2b1RtpmrFk+wtDgfJYuWUn//PlkaR/GemJIKsIflF7uk5SAciXd8XHGnt3M9tGtTE9uwxUdYhSza6WMEDCdI40CCbg6aLsiRQbnsR5SrYhBo7Sv4tBefAzkBfFA6qlkz9JQzZ5tseKBBPFLIlSHx2yLU+WFGk/U4mdAoom6EJl/kCcoRmODk+lK5Y4qW7ESH0lYgZDCdJ0qJz/XGmmDjI69lDATPcpAEi25UgQTMb7qh0MkGouiJKpIopOqBQAIqKCrOjzMPslUlcBevTFRBSwtlNU0dR/0N2lkBrRUbHULGojcddc9XHDBBTy7eTNr165l7doDuO22H7Fu3aGgDN0QmJocZ9VLXkbaGqChNbEZCSU9xymlPRqNN1q0D1EySTy+2iaCR+keB0OUujpqmlWEolNK2r6gqfNs++fN5+TFg9zsPDozqLKLTVsUhUMlLXyAhspwPofEUESDak+jfckHXnoQ80aGALlcc9Xa8gT8fyOR7W7VrcTq1at7DnBlWfLjH/+YV7/61XzpS19iaGhoJ0UzzBL4ns/6r8bNsyQ3xeGHH85tt93G6tWrK3qDQ5EQo0cFodh3pqYZnD9AZi2tPuEH+bxgujNOyCFJ2uATyhhBJSgfiHjCTCSPOWUUkLWvfxDTsPRlQ0QtqmqlJX3QV/dGjCKi01qjXAQSsVP87dBAXlgLEytSkJqzIWpvU8mqmNUaMMcgR6TQkuSlQ4YmkFSkGuOlGklUJoeLj3KTO010Ch01wYHyllg4VC5z9xA1SXASz5AGmmlE6VwMVXwUd64ohiwxaIoYBaFWmqCT3smsg5eZfJFQdPOqDZNcXher8i9I1VGPw+oKKyqHdyIkJCjymTb59Cg+7+A7eU835Jzj4Z8/zGvPfR04z7NbNvPG815PX18ft99+O+sOOphbf/B91uy1kpvuuoely1ZiVZd2Zxq6bQniilKBxSBPnoRqEmWoeDgCYBPl4JbroEhCQiNaGpXWJTpPKB2xCAQivnS0Q8n4+CTHnHQ6nZ/dxujkGBNtxY4dOxgdn2TH+BRTZZtnNm9m2/gMW5/eyNRTT5BvfIRNP/oBZ77pDXRyQ8DR1+wXr5dYY2bPHZ3+qv31azfsHIC1lu1nWcZJJ53ExRdfzBlnnMG2bdue46xea4mez9rdiHf3r1GjtWXBggW9UKve4YGhjDA91Sbra+FyB8ESfMQklkbfMPMWLKS/vwW6X/xqo8KQo9NIQ0s7r7T8jmk0zExP09k+yY7tzxBysCohM7aK0zBkukHDJnhVkiiNok2qFc4Edok6eNHWC2eizqlAYA6QqgI61tEJaucxYqTKmRXfDq+8tDxBZulWnpFSW1iDjx1ikIujaldqFLHnfKZItBdAFbkRtJi61edWRdSpGLJobITCKIzXaKuJpRegdG5/XoFWtT2BiZpUG7kxkTrRAiUeG1NChLRi/OkYaKUJKkkIRQfTkGomIAfM4iV7MH/eCEVRyFPCWJwvuOiii7BpwqOPPcyprzqTG3/5DPt6RSNrUPopbJpSFDlWW4y2EBQBj1a+Cm+ubhglvid1RIXRdSEl1y9iUL6Q16o1IWq0d1VOsPTMq9bsxzteuh09kKHaDh8tUzu24p98hNe97nXkeT95XtDfWsL8gSGmXcEgr2DFsuUEr5icmKTZzKSK07N1h3qRwknmGjDNpaeffvrpHHzwwVx55ZWcd955HH744ZRlSavV+o0qkF3/vrOaOrLPPvswOTnZA3qptE/WJqisSf9Ii/bEND50MRax2PSKaFv0D7doeE/E0Z6coiw7gh0qaFjxtimi6JaaacQVEIqSsW1PYmOkMJbFC1dKPIcPPQynxMn9hhMrSczvzhSmp8ad+5HqjVUimPN+9gnQG/EqOWh0FbZjVJOI61F4Y7SEGIRM4zwhZihtgSgu4zpiMk+IqnIZVxhxPJbjwYtgLtga6NUkyuA0+FCSYlHaYEo5BHy3INUGFx1UCtgYa4PdiFVVfaJECJWoKjkvSPK8xVcivBqfEZs64xU6VSyqL6+OBOfwMTB/3jDOFeR5SV+zgXMFd915J0cffTSRkvPe/Ha++73rWXfKyegsEHHYNMF7V/mtChlPqyijPK+xaLpVtk0AsqDROiWGAoMknYWoiDXhS4vCAb4SAAAgAElEQVSuwhjQeKKKWCRiI2umlKXijNPPwGkkpiOx/PyOm1m+eJizjj+eWcG7PGl3FJ4h7aSlsorRHTuYNzQyC6pXe0ap/xrEfD5rrgnyrsZMS5Ys4W//9m+54oor+PSnP81nP/vZnk5mV3OnF7LqG7R+Dd1ulzRNGRkZ6X2OorKrUJYyywh5iXYeUuFiGAXeuqpVSbEmgs4YXtAgRgFHO1NtJiZGaSWRxGlMEihLsEaiK9EKV5T44MSZv1Li+srXRqgLYneRhbR6gL746zdqYeaOcWG2P41RclpFoCYf56LYvvJ7rKuU+hcGmXEHIkYHMg1pGkhsl8Q6jCplwoAobFUs0WSCq3hItATsqCDiIyGsRSij5J/4asZfuZKJA1l1sc0c3gSVY1iUflYpecOU9hDL6tcu0SpgjSM1YFQmF8FailiyeesEeyyYD6GU0WnQlHlBo9UkKhjo72P1fvuSJQ1WrVpVjQo9A03LzzdtZdGStaBaUDhiqchCkxgsiW6gfCognK5Ba0MjWpJgSH3lfRnE7jGGorqRhQcj75XEb/jqAIxBybjbR5IgsZ4uL/CdLrp0dDrTbF7/FEP9QjZTOGKEjgvMFJ5mhNSK4YIlMrZjB0PzBoU93Bv5v8Adusv6dRjJXCbp5Zdfztve9jZOO+00tmzZstNE5jdZ9cOwNmTedVQMSIyD1nSKNnkhXbyLCry0UWUQ6oEyoCuPkBAj3gViVASdMDhvkIXLVtDXPw+ValwpU8hYUmUue5yKqABRzTJSdUUq8zpWsaHSoqvfAokMXiATFXjOhpg7Row6EmPZc9TSZvZzep8fhbERUbgKQSbWlYYcMNqEqrWolJOmGkNWXpFeQ6DExqwCNWsijZBrQBO8J0ZPrLxW69k7SnwtvKraqgqIrVmyWluUCthEWKwhBIoq/S1NxF4gGkH3VaLRWcCkCaZToNJ+7vvWVzjmZS8VcEJBkljGx8fJskxwlOq3UhpWrhQHLVfKjXzXT3/C3T/4T/qGLHYgRfU1mLIFFpG4py2DtQWhLDFWIi9qPEAphaumWworvIMYoa4bQsToSFT1mLViD2vp553XaDTRKlxUTPkCpZs88sTD2OEFdJVlPDimuo7xPDAZHDM4iEl1WCRsfnY7Cxcu/C+xhBeyZsfTs2vX1Ln6Zn75y1/Ol7/8Zd773vfy8MMP9z5/V3xk7ut7Pq+x/nk9YuPuQqgUEAtyLbYARmfERPxPNbNRroGSUD3QlA6AQwePi44YS5rD/YwsXMbAvAWQGkpVhZ55YW+boFGVAXgIAZRCYcmQSV0I0s7/juXC7NIjzuWBRPkFlTKUZYckySjz2aK3BvqUjgQSjA2oYCQlzloRgIWUUjt0paKdpRZr6kBmbSCNCWm0RBWJIRKC7009lJFMl1obwxytQ1RgdMSHQCLhD8wyfQNaZ5RllyS1+CLSSBuE0KWFxQUp1YvKPVtFeYp3OiUGy8TYU4ze9zPeecKxrNp7Fb4Ak4H3jmeeeYaBgQG0mIVVfemszVyWZYxu38y73ngek/k0G79+DesfepL99tufxybGWXPUsSxYsTcDfQ26vkmaQCg9Nk0IRQ5aOAOJlTEzyqG0Ejl4DERTG1ZbDIbZkPTKU0JVGA8G7x3KanTh0CgmntnA4qXLaCNP0DLkhKAxQTHS3yBQoKJBKcO2bdt2ysjp7ZPd0Mfn/nmuHmXXSuO/ms7MXbXaWSmxN7zmmmu46qqrcM7x3ve+l8WLF/cOi/ow2DXgfO76f/nZAY/GEJwnTRt0p6crR3qH8Sl1ZrCJ4DBoHfFaPHIUEXwkGiXfQ1uCtyhtafRbbKuBVpF8Zpp8uk1R5sIxoiTSRJk2IRgUFo+F4ISDVbXzvw0x3Qtjos4BT2HnjQFyQpfdLq1Wi9Fnt9BqpLNPfua+IZ4YDVYVJNGgdUn0MikxQUDKhlJElWBMBJf0el0VFTEGgpcZtzcCIlFHM5QiZba2UXm0ztK8AxKwFJTHaDG81cRK1OTwXpy6O0WHZqIpyxlaaUIbR1I2yDJPFhOwhu5Ml/6hQVR7kru+8384vKX4wDvfRHRJlcGS9hiwGzdu4IjDjxQYovdm7my4vH79epYvW8KBBx1KKNvc/MMfsXLvZXziiis55w1n8K0ffY/70SzeYw1r1u5NpEmIntJ7gUi1xuso9gc6oEkEXq4qLBNSvPJEr8W0KEkRsZsc/CF4ovIkJqlG2gZlAts3P0trZD5l6ckjhOApPCxoWqySaidUj5bt27dzyCGHPKdSmLtHdndQzNWg/FdA5q9b9UFgjMEYw9DQEJdffjkPPPAA73jHO/jkJz/JsmXLel4fdSzmrzpEfhU3ZHdLrCTEU6UMnoXDI1gtXKTEQIxWRu2hhTE5qBQTCqICiyYag8PLOEBHNF4wNFWSxET0TDalNbiYPJ/AFxLjYbQiRCt7P0asVwShqfYwqN8dEHWXV1IbCkEFMkVwXjGcNvjGD27k5FNOwxrV44Ro4ymCJ7G6qj4qmrjR+BISBVEpGjEhKFNlZiQ4l1PGHKNSlJKZd1RCpY5eqhzpGBKInqBEdWurYB9dZWvYIIQqoyAGQamJCYaCkChsiHTzNlkzA5XQTPqZbI/R1fLcLp59iluv/VcWLB5m+aFHs/WRX9Job+bSd7yL+cuWYUggCT3HKhQU3ZLJyWlGFi1AR1Udws+9Se5/4CHe8IY3VCBhi4XLlrFtdJrSNlmxal8uWrKKyYkxvv297/JPn7+KfV7yUo54zetZ0Pr/mXvzeMuq6t73O5u1m9PVqb6KaiigaAtBxYAI2BCNiBLBZ9QbMY/kKnqDxhujvMRojDfPq4nG+ILmJjcxMcT4EoNdbOnsAMGAoCKNAlVUQVVRzak6dbq991przvH+GHOts8+pUwhFkef8fOBUndrN2mvPOeaYv/Ebv98Qk0VJa6CF7U2DU0MqMY6WyQhGU/foCigdYgqijZhYUlkvWhcwRo9vxBIp1Q6j5T1Fp4Nrtsh7gnGlWl/GwOJmo56c/Q6FlYDPfPbnk6lwHM6Y39PSzyo99dRT+djHPsZrX/tafvd3f5eLL76YZlOxq6rJ8VDj0Nc0V6hYb4UFAplrk5nIVCdXApiNFDHSsA0Kk2uXuVFw0wlgLMaUqsWrDRZYG5VGkIBHUxRI8PgsEhsDNJpGOT5R2dtFZWnqhJC7ZHD+xI5mhzMOD0StaV0H18ctqhfabDTo5tOM7ztAo2mTLobRJjlRQZ8gqjjuRGgkA5ymhygliMcalRm0iV1njKNhW1RK7s5pY5IeO1Qw2NCsEWdrAt41FP03CoZmVnkcSk8LShWOBuOj0uKDJY9dFg02MaFLyCeZmdnN5ptuYOrWr1B887Os2HI3yzoH+O+vuoATO7v4q9+7go++73+wdtUq2pIRKp8bSEeUSGsgY+vWrSwaHCaavuAhzCkhP/roo8oNEMhjYNXyZUwfmOSUU04h5iXNrMWyVat4/a+9li/889/xitM2cuMn/pxud4ZBr5IEloijiZUWTddWXdNUFTN4rEtNdLbAELAmYo2WAB0N1XE1uvgzZ+jlOQNWmZy5FToGpPQ0GylozMukpqenWbFixUEZyBMJDE8leFTPn79Y+jGS9evX841vfINOp8N73vMe7r77bkIItFot8jyf89wntugqLZT0/ihZEmNSp21O23u8VYDUS3LDE82WvY00xafsS2kIIQXjmNTcrSjhUqHwDHEFRexpeTYZhoFKEDQUVdXeJlvgfQNnnxiJ73DGYWUg1agWgZHZ9NPZDIJ2mz6661E63ZxuN0+iuoAV5ec7RYbbtoGhJEqJNwKS4X0gFoYyRrw3xFJFjp3aBesOnyIrNtPKTdTzpzOCNTmZDBJNSYalND1MVeYMEZuJPg6wRsgDOAeZWELmaMcGea9H8AM8dv9P2Petf+V9v/9ulqxcAUARO3zl367h5E2nsW3LNsRYGs5QJpWzqr+kwmswUJYFjVZTafB68/TfU5cw6I6Z5zl5GfAefnzX7ezfN81je3axft0adjy2k6PXrUei0GgPk5mSC19+ESdvOon/64Mf4tL3fQCKDsEaMvGUmRo6Yw3GKR3fSElZGpxLVpNG27+N87oUknJV5hqUYQZrBpGypDUyRLcMNFsNYgh0i2lWtIeAAqj4FbobxxgZHR1d8AjzZI4D/c95MqN6Tr/LX39LxejoKJdeeinWWt71rndxzDHH8Ju/+Zvq69s3fn52VGUf8/bhVB0ztmAwa6aNrKUsYuMY27mNJStWYBotrGREV+CM6teKhUYiWnkMNgTSZMELeFdSitFmuhjAR0yMSqF3KmCVNZVmEK3DStBysQlPCwZyRGo7/VG/CEqQ2je+hy99/gs8/wXncueP7qQIVZs+qekHjFFtUIzBV1wRG6BUdNoYAzEgZtbbU5KTXYyF1r4lpi82oBJuurtG0f4BCWoDofZ+BpfpWd9JVPeXGGgZwSYxFgkKaMWhYX7y5avp3vJVPvJn/w+LVy6DkCMRMjvIpk2buOee+3h4y1b+9E/+hK987Vryma6qY0eZ7RcyUISS3bv3cvS6tXNxAGYDR1EU3HnnnZx66qn85Md38z//54e46bu3sG7dOkJRsmjJUjV7tqhYsomaAQocs2EDxy1rMb57NwFtBShNiUipNHcTEHKMjZjMYDNPs5nRaI7QbLdotgdoDTRxvkGjMUCz2SbLmkgiz3XzDqMrljHdMxzIe0z1ApPktGxJ2etQ5oEQAlWrSaVNeqgdvP+IUaXXIkKv16u9gOHwjzMVTla9RxU8Khe4CusoioIPfehDGGO46KKL2Lt3by329HjX3fdOCz5OJBCBbneanz24lTI6ylJxjZmpcYqiYP+u3YS8SP1ElU+0ZqIhHfuKTpfxvXvZu/NRdm/fxt6dj/LYY7uRosQ4RxCnJYDEv1KRoUCMGThVfrdGiBXF4Gk4xjylDKQCU2uzbalsHQJLFy/hTf/1cvJuly996EucdPxJKi0HVDfeG5L7liU30DIWEUdhI7YwBKPmwIYA4mYPTsbgbZsyBjKTgRUkVn0tmgU4r1oIMSrhK/qKVa//XopR5/pEZjdVK3yWQcjZcfNtlFvv54//9KNkzqI1W19f+7pjNvLATx/kec9/UT2x/vaTf8ett97KqqNWs/aoNRx//PEMDg6yZ88eHnroIV7/+tfPuX/90nreez796U/zwQ9+EBHhzDPPJITA92+9jTVr1rBqxUompia1rwFSN4v24QuWP7zySj76pe9w9HPOYnRILSpC7CB+kBg7WKNftS2aiO1RhIjHKGcmqNC1b2oa7GNEYk6rMUjDGnqdaY5esx7bVjA7Nj1H9YTBRkNL6FUglEhZlrTbbaanp9myZQtTU1Ns27aNzZs3s3//fsbGxhgfH2f37t10Oh2Gh4dZtGgRzWaTwcFBLrvsMs4555w5gOqTmpNpkVTYR7/zXxWQKqC00WhgjOGKK67gV3/1V3nHO97BBz7wAVasWHGQhsjCmVOVgcziIDEJZplY8jd/+7/Z8MxfwnmwLU/m4cDkNC4URAKdAwcYXrOaXlfZ3dE6besoPSGfYs+Oxxis9LhNJIogvZL9+ywjo5bmQBMpIuIs6tRsUhaTnBGMxYp2qxuxNe/pSI4jcoSBflQ9YqKhCJFuqebAl172Bnq9HsPDg1SnO1CHLp+YrBXLE19gyojxBhsyEFT23oGP+hykRMTijfq2ULPsAikaECTD20jWiJqlWAFRzYzSGDJjiHWrTsJnAMqC6W6HHd/9PB/9iz+l0WgQYsQgNQgLcP755/P+97+fq6++mve+971cdtllXHHFFbz97W9Pco4Psn37drrdLqtXr6bZbLJhwwZgdmetJuWuXbu47rrrePnLXz5n5w4hcPc9P+HCCy+k3W5z49VX87znnk0sgvqNQJ0utwcHGS5nWDKymLyY1PtqvZKYSoPxCst5bzC+Cb1IEMCpI1rmoSgjsewyOVMgdOh1Zpg6sI+f3v4fHNizh+u/eA352H4O2Bx272XABZp+sM4eer0eg4ODFEXBVVddxcjICMPDwwwNDfHc5z6XoaEhBgYGWLJkCYsXL66p5RXJqygKrr322lqE+LDmpDEH/Xk+T2ShzGbFihV85CMf4ZOf/CQzMzO84hWv4NRTT6XR8GRZkxgKbGXeVI+UGdeaBYpr5GXO3/713/DAvh6vfvVLsRLoxpLeZKQspml4SxBHPjNFPlUQG1kC9dV0qiw77N+7m6YvKI1WVur7YQpi7wCTYyXGDNFuLsNREoyKZwUCnoBzhrIn0PCHzASPxHjSAaTqN5l/SZVgi8+giIH9+3Zz44038pLzX8LyZeuJxQQhRp2w0SkQGhVwxQV8SCLI4upMIQbFPpSoC9YKEgusdao1WQjeaWamZCjtVjXGYELA2QY+aLu9kVk/2mbC/YKUOGmBCzWd1lrDrvvu4zd+7WLazXYqjWq/irW60xRFwbHHHsuuXbsAuPLKK3nDG97AX/3VX7FixQoGBgbYtGkTp556ai2nd+edd9aper+oTVmW3H777YyOjvKiF71ozsQ+cOAAu3btYt26dXQ6HX72s58hEsiq0ivU+AnGs2ntSh7ZvZllS1YmINpA1CqUNQbBc6CcYexH93DHHd+FskcxnkMxw/T4BNYJIwNtli8ZYfnqlYy0Ghy95hjOfenzGL30VfhsgOFmk2xkUEWjosWFAmwD651KG6T7WPWeVFlAlZn2V2aqRVFpd2RZVgOZT7Ua83hjoddtNpssX76cK664gunpaT75yU/yx3/8xxx3/Eae/cxncd5557Fq1SoGhgZr6cD5FRiAwgjfu+lm7tw5xq/9t3diMsFGGBkY5NHdD2MReuJxNqc1MELWshCVTOkw4GDvY3tx5JQOTFB6e8WHUoIadPOC8rFxjlrbxrg23grYdAgKyunR+6iOgBUD+QihFvV48r4wgnq0iKvP+ZVhjmouBDJr8K5B0e0BcPNN13L2Oefg0fNnyFQhrPSBhtUbU7gSK5o2iu1hyiGs6dGwGRJzDDaJtjgkGoQGzayrhKmEg9io+EAQwVgHUmiVxZb1QrPWq7ixM7ismfpyKiagZSrvsm3bz3j2S/9PzbDEgwHnG+kIOWvNUE3EwcFB3vOe93DBBRfwkY98hHPOOQcRNYKOMc4xj5o/eWOMPPTQQ/zO7/wOMzMzeO8ZGBjg7rvv5n3vex8f/vCHa/X2JUuWJIexXLVaiYn9KVhnOOOXnsPX/+UafvkV/4WWWKZsjpUEcJoSegW7HryPqYfu4gOX/xbLli/GuQbgKBCy9J2mOUoEHLO9LHMAuAo79I5K89S6rH7IQnyKipexUHCoeBjj4+NPa/DoHwvxUEZGRhgYGeYP//A9dLvTPLz5Ee784Q+44h2/TelG6O3fw/kvPJfnP/d5nH32GTSagzRagyqg1e3h256v3Xgjl739D4gCFIFoI91uThGmcK5JkBlc2aQ9OABYTBZolYYgjtCZIcgUEixN34Q4Q4m6NlqTJCmNYcBaSkqmJ2cYXtzCOJ3XLloKU9IoHcZ5vGSoml8S1T7CjPbDOsLM7+xTvoEuhjKoj6tK4w9z++23c+9P7uHcc8+lzAtMqkKUCA0rdGys2alaElRQSaRQpS3K2lpROR5BG+y0IKPet2iOYl1BkIA3LaJ0scar3oWo/KCpX1+7c21UkyqnURFMpGkMjamJmh9AMgGf7enQDz4xMVGfk621POtZz+Kaa67hmmuu4YYbbmDlypU451i6dCnbt2/nzW9+MyEEvPc1oKeSAYFOp8OnP/1p9u3bx+rVqwkhsGzZMv7pn/5JfXDTef5Vr3oV1133DS644EJiTHwNIiFarM1ZsniU1UWOiT1mjKVptXIVimkcGc2BAb78d5/gy1d/Ct9saWFQhJBU36Io61RMxJApAFuxO6qo0v8zzYZUJHjCAP+hKjGPPPJIfbR5uoLI/Nc9KKAT8DjERNrtQU7edBLHH38cj+3eyQkvfwPiI1Nje7njvnu5/u//TbO4PLBsaIBQFsTuDEf/0rnM9CBraBdsiEKkhMJQ2BIXHWRG+2iswYslmBJjhKnpjmbjYglFSaM5xMiixTSbbUKE6alxZib3IlJinDA1Mak09+q+2qgMbFNijAKqQUrFR34RMBCT6tM2gY/179OZZqA9woHp/cSyxKWJ8GuveR3j4+O0BwdpGItIgScjiMHjsdLUxrSUGmYmEB3kkuPsIJiuaj3axAkxAY9B1C2XgMEYzU4yLCU9vPHEZCAVTKmiQNZgyNLzfTovKvuyWqTeN1jS8pgsU5kB06co1veBt23bxllnnTX7+Y3hhBNO4N3vfnftBp/nObfccgujo6O1Nysw5wjT6XQ455xzOPfcc+f8W6fTYWBgoHalN0bFa/7mb37A1kc2s2H98eofglcDbRp0el1e86pX8qXv38wpv/Qi8hxo6lHCYigEjl61ktJGfBmJ3mjgdlVbfOrpSFUB009/7v/8C7i8p7rKQb9faByKpv69732PjRs3zqGYH+nxeEFJJB3PjYpIRauZtmsYVi1fwY5HN3PKM57J0GibDSeeQnQBSVq0zjexZUGvO0PpwYsnD2p6ZsUwMTOphMYykBuh6Ycw3qnKo+0RxSgTtcgJpbZaRCsMjSxmcGCYngjeGkaXLKUoZ4jdLoWU5EUBMU8Av/JESPwPU4ZEX3jq/JpDjSf/LVWkIa0gAtRt+gBl0cEKDLTafOdb17N7117arQaTk5NICKoY5lu1h27pSvDaVCciSvNOSHZmwJpcAUFDcpKLujNGo/5zSeeiygywkhrh0nHBCtaahAEEouQgGgYVOJ0tJRqBGHoMN1oQjZooGxLIMovwhxC4++67efazn12XC/uFe0ExABHhrrvu4oILLpijmVk9JoTATTfdNAdcDUGNyAcGBupW8X7H+MsvfyMf//hfcdVVV9HtTKfdTbOwdrPFhrVr2HrX7dz5vRswDcFKg8y31WmuO8Wxa9Zo6c87rJgUVElePws0XJl5PwXqxsT5pKt4MG+i+vl4bMhqclcZ2ELB4+kEAudcR9/nnTVxjSwaXUanM4OUDlM06U11yQ/MkE/3mJnosmfPbsYnJumUQpFHYpZRlhGkQRCNuXnZqxXl2gNqteAjWOPUPUCgzAucV7NsPLRaQ2ADDSv4jKQhY9WIPrc0XCSWBRkNbZQ0gSCuljd0FohPTys/HLYeiEptz06XJPaTGYx4bOZZtGgRf/rnH2Pb5i185Wtf5eyzz9ad0Fo1e7bKeDRlQ42iUL3HIuaIaRBNobVtozaV1Zspj8STWaEQg7f6pUtl/5CwDAkk8swskKc966rlic2IsUCMx2MJQd+rM7WfZYuXgDOpoze9btQgUh0/HnzwQV72spfVd6ACDY0x9aK/9957Ofvss+vGLqC+lmpBbd68mYsuuugg4lOFe8wXwinLyJ9+8M+45ZZb+N3//i62bNnC8PBwzb047rjjGPSRPff+kO1r17D++GcQRTVkJ3ZvZ8Wy5WSuNdtkmEZIpLdq0RhjUq+0qRsR4WD2cbWwkyJivSP1k8YOmj+HIGjt2rWL5cuXP5Ep+JRGfc0L7MoxqnpXgtS1jynJI0zsT7IArsCQgbHYrEGUkmZDrU1V86QkzHTJ2i1Ct0OjnTGxu6e4ULBEiZimJ1L1rmRYUbxQ/QwikOFA7U9CwDhPDAU4T+Yt3SKo7XCVOUqh2jhR9XmDRD2KSeBpSuaAw1Qkk75AXf2hak92IsnWQeh0eqxau45nnvYstm5V/5OyLGm3fBL81YYhCWiaVZY4p9piGSrxpiCpZhiGCFYr3lG05CWiJF9rPVG058AYwbj+Ep6e9Sv3OuMzTBDaZCmNcnireiEP33c/v3rSMakBbjaF7xeSnpqaYmhoiKGhoToYzFe7CiGwefNmXvnKVy545o4xcs8997Bx48Y5O+58HkP/64oIzaYDMp73vHM5//zz69eqZA+stfTyacpOj7/8h3/koTv+g+e+4HyylRt46L77eP6xxyESUvetSQmW9gXFGJUP07fm9eg2t1NWL4a+SaB/7p+nh2qK6x/zjzL79u2bE2wP9binOhY6PvX/W8XGVTNwwFiarUwV/11IRz0hRkMZ8/r7qzCpIhhVsCuDikCVgm14TKHOfc5HNYOSqDwlI8RgUAmMREoIBozH4JDUD2MiZE6odHhd1B6aSqyrEvnGRrx1FCbp46Y183SMw4pNdft+/T89NxtjCKmM2pvucP2130CKnONPPJYzz3wuQL0gomjfjIsOm/QkVJNC0RVnbd1BWv+XviClzIN6nlQXkJTJ1fshpaOi5UyjpSyLw5Ro2dhYGo0WRqgnrXOO8W07OXbDhvTBZuOrYTZtv/HGG7nkkkvq5qv5kzuEwHe+8x1OPvnkOQGjf5RlyRe/+EVe8pKX1BWagxZpGlVQUeBVH9dqNerfa2k0S/iJodVs4tqDvPNtb+O9b/4t7v7KP3P9P1/NAw88wKZnnDrb4DZnJ7Z9XAP9MdvnwkGfoyiL+nP0X+PjjUMdZSrQfXJycs59+k87tsz7uxGL0vOqkpRlcGRYu72l/6ga6y7vOkMzVukEiaKoZEGnDGcjZC5iTALuiTgLTvT+h6BHbI/BOkOGT1o6EW+UsyTWqe6wBIwNSVw7mYfZWC/OIKnoEC1C+YsjKDQXKJv9qRXoiMQMCZHvfu+77Ni2lUe37qTMA84qbV0kEGJqDhIh9wFxLaL08BZNH01KpAs9ElXs1RALoii2EKTQLMaogvXseTtCEKIU2h+Q1MDFJKuJzGILPXN2C5Uc6MUezngaLcfYY1tYuWyVFmXQRRLLkHYjYXp6mrGxsYM8V/WzKYYxMTHBjTfeyAknnFDbCfTTq/u1KCoPE1h4Mi+0+/bv7gr8+tmFaSIxOtrNBsY5BtsDvPc9H+ScTUfznS/8v4yMjBz0Xt4kroUAACAASURBVE9mofZnRhVYXPWY1M6EC4yF+mD6r2FsbIylS5fOef4TyWKO9JA+bCxglSJgSpw4xELwiht577HepUyOOZ/dJVEsBWQ1MBgf9NWDWi6E0CPD470lSKm9XqaBN+rh402OlbRBxQYSLERlqpZRaDcqmnqoe8RiYSFYtYcwKhIuODxPXyA+jADiqH0K5qVFNnqilAo+Rodxlhu/eS3X33gDd951h2YoQSOvtiqTPG0VyQ4IJhhC3RMgGmWNyglqtLdaijUqVlxhB7pzJNaqSRPdgOprGSyWzDoohGaziZRKPOtM9Wg1hzGxxwO338QSq70MvW5qYjJgvfZSTE5O873vfY+LL764JkXBLIU/z3N6vR7vf//7+YM/+ANgtqW8n10pItx77708//nP/7mL91DBY/7f6yyNytZSvXEllVlffP75nL7xpDlHosNdoP0Mz5tvvpndu3fr95VK0v19KNXnnR/05o8f/ehHnH766TQajb7NYO7P/4xhsNoFa1xaHCo5YTLH+O69ZBX7NEpqIk0/UQKkEc0+9GfABoMJJU0/mOaxIxApO0ol0ORD+1li3gOSm4EYsmaaL8z6KlkcoSgRY3EOHA3IVNOmzoRE9XuD6WmntfWzMqRHeBxGAEmszSq7NdS81Cg5AaHhm3S6XQbbAxjR6sDJJ5+sb2gtRjzOqop6ZpSJ1/DgxajvrXgMUfVC0mIwaOqGU8k2QT1j+gExMRD76q2ZGLIk+5Ylhy/nDXmu3BKTF0QTCd3AD775dZ6/rMWLz3oWptAjQkQo84KiCHz+85/nox/5c0477TTa7bbuQH1aE91uF2stf/RHf8Q73/lOhoaG6l25n31ZLaoHHniAs88++0kv3oUWU//vql4MBAU/RRd2M2swumzpEzJX+nmj11PAb2pqine/+9189KMf5cILL+QTn/gEvV6vDxOYBaEXyiz6x8MPP8wzn/nM+ji30OP+MwKJSMVDom65wAacsezfPYahVFzCoCTD6pidVPnV+c9rEDEOnCViE6/IYGzEYZmZ3k8sSpzxWFGJzjzMIFGzXjFCM2slSYBZ2KAscyiL1O9isb6Ft+pCJ0kdLxApURtWrFFw98kQdZ7EOIwAkqTvqiREVJsbFMTxeIqyx6bTNnHGc88kGGHV6mXccccdugtr4ZQYtBxYZRAhJI2P6ClNCcYhIdf3MGXCMiJetPPQicdGSzM2aKKOcVl0tEMTJ0YzFMA4letTWiAUuZBZo6U177FNR+G6rJCca6+7kf379tIpcmY6M3RmZrjtP27nbW+7gnZ7kPe+749YtWpVTTKb3wx3//33a+PbqlU1plJ1gPaXe6tu0P7y7OMt7IUWX3/JuP+5VpxmgNVjDXV2tHrp8jniv4c7ms0mIsLo6ChXXHEFF110EZ/73OcIIbB169Y52Mz86+4PJv3XMTY2xoknnrhghvKfeZQxOJU3ILncoxW+MnSZPjBBhidKFyMRKwWZiRhK7QSnwEvqfE62CqAsZtdsYZLuR4wR56EzeYCy7GJNgDyn05nE2YaCtdFhmzY5IDr1/sIRp3NimaudSIypzUKofYCMx0VHAzXnNmIT9d7Ua+BIjidPJFNY+uDfCxRByCgx1rNxw3EUpmBq3zh3//g+xvbu5pxzziFIpOkdSLJjEKMganrt6NGeOLHgFFDVaoGWpAwGH/RoEl3AiCOg/SbR6IV4sUjQxaPyh3pzgwjeW3p5l6YfpAg5caZLL++wbsVqLvj11/APf/+PvOIVv0qn02NkZIDzznsBf/RHf8yyZcsO2lH7f4YQuOGGG3jTm96kO36zWQeK+RhIhRv0HycOxX1YCFStrqHCHuY815BIc3MfPzMzw9HHbOj7Dg+/ulGd940xvOpVr+Ltb387Z599Nps3b+b3fu/3Hvczzf8M1Thw4ADNZnMuILkAbvK0DzNL2LSpjKtGiRlT4/sQ6zQ7TnYiEiNOAtgqWFqcVbP2TgxkElNVEFqjI3THx9XX2cBUZx+9bolkBVI4rIi+TrJ6bA62kdIQRbfoQMne8d1YB7GI4AyDg8NaeDCWQMqIrVAGqSs6kaR89ovARNXdQebEkOr4YKz6jxjvKEOXII5Np53O+mM2kHeTS3hKqSvgUJzBmEwrLhbIta8jpPQxCaan4KGscnWfrzgVqdzqLKYMCJHobGqZtlgbESPaeQrEsiTLGthY0MqA1iDf/dQ/8GdvvZyBwRZXvO2tXPG2t9bZgVaGIEZLNYfzPGd6eprBwcHauf3WW29l06ZNOOdqD5IK3Oz3EYkx8s1vfpPVq1c/afxjoe/ioYceYsuWLbz4xS+un6O7HHXzH8DOnTs5+uij6+cdKjD9vNHvrfKOd7yDyy+/nBe/+MV8//vf54wzzpiz2A+18Pt/XwXZ8fHx+pp+Ht386Rw6vauadDqKGU8jy5ienqbVDDBjkqpckUojfeeDRJAUYxlwhR5zTMTZjOHBQfKJCXxptGnUCJBji5CkNQVJZmF2oIkPBmugjJqFOCOsXr2GfbseIfgSS4PW0CDOQh5LPXq52U2ql3dxsdICUcGiIx1EnvQR5qCJN+d6bJ2ije3aS29igoceeJCREaVyO+dSd602sGklJlBJ+omoJYSTCihVRp0XT4aaXuv7O5wzs/aZ0UCp5z8jgpSz5+hodYfOvIraNhpOhZidQ0LkZ7ffzKvOPpNFo8NJ2X32C6iOHyK2JuPs27ePiYkJtm3bVmcVW7ZsqRdQu92es8iqBVEFD2MMX/jCFzjzzDPnaE48mWNFP1dk3bp1/OAHP6iPNMJsYK06Y2Ms2bVrFyeeeGJ9TYfzvtV7Vs859thj+f3f/31e9rKX8ZnPfIbzzjtvzmseauH3/z7GyNjYGMuWzfZz/P9RfZm9NqiKA6b+CSOLFjE5MYZEi03ymEGUp2GksiKp2M+ezFgkMUxBY8xgazEDQ4OQWaxpgPFawq1IRmmXExosXrwU59NcdKlqJyW+0WJ02XKCsSxaskIxGKv2pthZsFVCxBuVBbU43Zyfhvv5pAPInPM4JI6W1EpjMS3k6elpvnPTTTzyyCOYTAHJqtRpU328KuVaDE5KDJZGJgiRJg0aeEJyljdGORy+cJiodG8pBQlFwk8UxIqgdXeT1Sg4CCY6nMkgWtqNNoWN9Ho9pn72I178guch4lNTmj1oAVT3PcbIxMQEW7ZsYdeuXYgIU1NTfPKTn+Rd73oXS5curZ93qHLk5ORkrbzVPw5nsTjnaDQarF+/nn379mnAWCAghCBMTEywbt26g/7tcN7XGGWNHnPMMZxxxhncdtttbNy4keOPP/7nZjbzM5Qsy/jpT39aP/cXZcyh6VMwMjKKLSOliYRkAi/kKoasbbKzTzZaIHAxw5o2jiZiMmY6HQaHlmGtI5dpJBaUvZKyW1AUBUoWaDA4MoxzQhEMUXpY0bIuQAyTNNue0dFRWoOD2tuVNqZYBmJZIDHNLasVrTLEVDg98hjIYbFLRBb+okXUENumoJKlXferX/gSZVkSY1SbxeoDS67R0QiZzQiiXrmVabXDqGlSQql1YepRRidbJPYpQmVO5fuMUVzFO90BrM3AQjQ5WKGXqPcPfeurvPttV9RZ03yDoIXAvLGxMa666ipWr15Nr9fj61//OldeeeWCbNL5z48xcvXVV3PJJZfQbrcPundPZGgB3NSZRkR42cteym233Zbc9VQVbHziAMYY8jwnyzIOHDhwkEL64Yyqp+ezn/0sxx13HL/927/NX/7lX/KWt7yFoiie9OsZY+oKzC9KAKmaJ2uipFhazYwD+/ZjnDbH6fwUSgQjkdh3NDBJLy5SEqWHmIClwPuIc4bVq9axaHQVjVZGNtCiOdSk0WozMDTCshUrWTK8UteFBIwrlXfaV9mKEYYHR4iUOEp1H6xsU6woXkih6v9J5T3GOFtVOoLjMI8wUmcemFncJ4SANx7jHYsWL2XJiuVgDVu2PFTvPC4mXxcrqfQkqXHNpLJt6n5NO5kTqzRemeV8RBGwDo8hcwZvtSaOibQznxiE2pSkCu4CpsSR4UyDZgZTYzt40enH0GwNYJ17Qml9jJG1a9eSZRmdTofNmzezcuVKhoeH5zxvoV24yr7+9V//lZe+9KX17/rv6xMJIhVLskb4jWXxYtVLVSGcf+DrX/sKN1x3PWUZ6/fodrt1+fWpjCoI3X777bVk4ytf+Uo+97nPPSHz6vn3WUTYvn07p5xyylO+tiM5KmFsDETjIGtwYGpqDvdIWaEqWF11hikfKWENpgRbEfySfYZXMHN08QpWrjyONeuPZ9VRx7N23YmsXLGWkdYo1mY1o9WaFthQY2kmOS5ik+iyCzSyQOZLvCvIjMG7Qk3VpadHfavIsBxpMRAO+wij5CQS30AqX1mcerEUkSXLlnLuC84FsZRlVFuGRK7xXvUOFB/2GhyqOrokXxgRyrKkCAlHNkLmwJlIyxky28PYiHUB72YDW5CIt0IwHhszjPOYYHBmSI82Mac8kDP2yFbOfuYZtQn3fAVvODi9t9ayePFiBgYGsNby+c9/nrPOOmtBkZz5f8/znD179nDcccfVj5/jp9pXEn68UWUg/dhTtRCLosev//rruOmm73DmmWdiTKAoFGPKsuYTdqd/vFGWJSEEHn30Ud74xjfy0pe+lO985zusX79+TkA81JhPZa8El47EtR2JIaQgbbShToxKzzS81daFGGp9UScN3dCS/IEjU9IZTgF8aUFIx+JgcTZDom5woVCNjkpEOkiu4trWgiggilQVvNlaR7W5QuybYwlvIyOYJCJuIlYUq/G2TFn9L0IZtyL5RoekrkXFQJKIbVDT5qZtMdmZZnpyhjN+6ZnsHdvPmrWrsWSUMadhGpQmYCWnTN4XobR4q2Vb7aSPRLzaQFhNCq21mp7ZPgq1BG1Hl5R6mkjLKt6hKkxqxB1FM41pOixqtSgbTRpGgNnU/vGCgDFKYT7qqKP44he/yGWXXUa73V7Q/X1+MHDOcccdd3D++efXxLL+xz9R/QuHKqTNainokbLdbrN40RKwhj94zx8yMrSUEAoGBhSoXb165UGl48MZ1THvuuuuqwPg/KPR/PLroYJyxYnZt2/fU7qmIzk0uY5YvPYii5ITfTJud8YQHLiioUeT6NW2QYz6ydmkdJWwfWu1kiion5Kkg4R20Fadt8pTMtbiI3RFdO6aAmvaeImUJlU/UXZ2xGFMxVMxenS3QkOatZxhFI+zQkmRhM9/ATIQ3fxcSkL6FlslCGSlJklJKPm1174aI5aHt25Oiz9o2RZl93kEaxtgG3inaZ4Tm45Jlqaz6SgjWooigaTRqOJYyPAp8jvaODuAFU8sAiHmCDOIFFoHNz2w4J1hajJPjXeuXozz4/OhMgJrLWeccQbHHHNMzfnof/z8o0jVr/K1r32Niy666CBN0IWec6iR8hXqDmEDxgj7949RRs3W9o1N8du/8zY+89l/Y9euXYQyp8yLx3Vee6KjyjKKoqjL0zC3F2R+sDjUfSzLkjzPWbly5S8M/lHf1z7SlUWJWo1WE2MEkQimxPigynAxaPuFCWonQhf1q+0iEshDTnA9yjInxpwYSv0pBSEU+udYUpa5GkalRj1BpQHKGNUYrEw6MmIwUY8zmbQxVpDoam8kkYD1DawFL6Lr42ki8R7GEQYlgTGL6ppE9KomUQjCQMtz3Ze+wf0/vZfnPu9szjn7eXW65p1mLA1R5pw1AR8rK8YuzjZwErFZxFrBG4O3PpkfeXVdw+NtIy0iV3uSiKiPDFYXlnNZasFOu4GN+NYgU7HHvT/+EYC2b0v14arXWfiOi2hF4+KLL67LvNX4eaSnnTt3smjRogVf8/Hes38YMwtiV1aS2x/dRqs1AMADDzzEb/y3N7D2hOO564c/4NWX/Rbbdu1hqjvzlBmoMIuBtNttyrKstU+eaAYl8+7x7t27a37KL86wqbLaZwoPECOSgEtciZOSaAuMAUdQ4L7i8EnAGMGbEu8CXhzet7Rr2jZ0XhpHlrVrEB9SCd6kDcbMPfJJ6uolgI2CdQ5JEhXBaHbtjKVXBLrdLjEUdKd69MJMn6jykR1PydahGtWHlASsNptNZma6bN66mbbLOO7Y4ymlh5RKRy9KyBpQimqjYjJKoOkNEjLysot1DidB+SJOa+rUBt3aZdu/8PonsDOWMoDxCmZmJiOEgmAgBMPwQJuTTz+dz/3zpzn15NNxLRU/jtapnkkCQavKUdX3Mj4+zj/90z/xK7/yK/WX3t8P0/+zuq7qdR5++OEFDaerUb3ez9uJI0IwgQxLEHARbv3+91i8eBEf+dhfcMeDm/m7j/01K1euwXrh3z73Zf7vD36Ii172K0fiq65Hpe/aT5g7nCxiYmKi7pP6RRumOi6CmptF5dcUJpLhKRAa1mnzXezz5AWcVbmKKIIJgs0CPi3gglLN3SVipFRcxQoQiCF54caozoxxVmsnhFCD8RJiyjaEJSuPpt0QJOGN+x7bTuZadMoZJDr2HxhXi5Sn4QhzGAFkVlBojjm0iWl31Aa0yckDrF2znl6vx5e/8iXOOft5tAcHEjlMG42iNeAKRAo92wOqkaDNc9EGzdYrCUInavVggyqQ2bTrV4G1qrgAPjPacEeDgp7a/CFpxwy0R5ZRLlvBZ7/wVV73utektue5tfLKjiDPc0IIfPjDH2ZiYoLLL7+8/veD7s48/ke1sO655x4uuuiiBRda9fcnsosbklKaUZUssZYf/ugeduzexerVq/nNS17JqtXLcdZTlCWXXHIR37r527R9S4243BHZMw46hj2R4FF9dg2Uip0cOHCAZzzjGX3BF+o5JrPPq95jDkYFsxjcgu9/eDYGNjkhKqA6W9ZtNBoQTY1pGKtFBMU0+rAyq5YlxglZzIiuJO/22Ld/L4gl0MOIYhgxKemFUCSBrFThSSLfZcjSsSltbM5qo14QghGkCOqoGDOMdaR+bCQFOeu1efXpoLHDYUsaQn83bvVFxxjJGrrgJqenaLUblNM5Bw4cwLgkxAJ46yGUWB/BaT+BsQGHIVjBEpBgycSDzRDyBFhZjCvVDNsm/3KNInoB1tR/9qUQKmuHpGxmQFurG02KXpdf/i+X8uHfeSMv+uXzGR4cYnBwsD7TV1kH6Bf8gQ98AOccL3zhC+tW/sfrjalG9bixsTEuvvhiiqJHljVRFH0h8SA1ERfALEA9VpHcalFEQjDcc/e9fOxjH2XV6mVkmU3MRKf3IsA5z3kOS5etwB3BOVRdp/750NlHH8m7rh5UDF5rYc+ePYyOjlLGgDcuzSt9nDGqn2+NXeD11A9WX7f/XfqCRlLTn38dcz9HCmpJQKg2OjepXwsHYiiKHmWhncbRqKSFiuqJNm9aW/viWFEZBZGCUpSpJGIIRRcxRoFYUc6MS5VMX98TSx40gAUxmqGk90EsVHYiRj2osbbqTceWFqmaUg0Eq53gmTWH5G491XF4RLK+LwWZ/Xu1Q0QDq1asZNnSFYgI5513Hrsf2zVbIzclgicGiFLgTSKPCanbMR2JjCC1p25qna4VwwGjNGJjVDTZYnBBcCjBykRBosEJeAxejJ4/fUBKYXpyL5mx3Hj9DWANH//4x3nNa16DMdqef8cdd1CWJddffz2dTodFixbhnKvLuP2jHzCsdsyyLHHO1a3+OkEqvQs7dzetd9mqlXz2vs7/yuocx0R27tzOSSedxOIlyxRUtgNYtOuzKIOuAxFWLl88pxz4VEcVPPo/+4KPYz443U+IsnQ6HYA6KFaPr4MDs8FS/x4RkjIY1YsnMmFMqX4CjYtiRo+moSCWXUKpuECUXH1cQnf2e5Pqel1NT4imSrUDjYah1yvw0WAkKnaHxybSGNFgcelzKMvaRVXW0wBVQND5n+ddTDQQS8oiEoOC0ojOEW9KrCnIbMAmqiTRKJAaXS287UyEMhAxKptowbqMYAMSI+IyxVqSh/TTMQ7TF2beriizCyfGksw6euScde5zuW9gkLE9e9l/YJy1G47GO4fBE2OJdwOUOIyNqK9nTAzWBnmpNVktfFmc14BQ1pwRj0iJtcn3QoxOLCN4adAjp2HVFskZRx6FhrdEW9CdMezY+SD/+x3/jcte/wZe//rXc/XVn2bv3r1cfvnlc7CIEALf//73efnLX87AwADj4+MH3Y9DcUAqbCCEwNTUVLpHJcZUGpVzAdjZP1eLaH52MqtkBpHpqQ733vsTTj31FGamJxkcWoYyUSPeWxpeS7YH9o8zsmjxobfhwxr911/9uX+Szv6bmbNP6WN1t1Z1eIjEGHBOsSqD9jAZYuo1cURTIol3oYZaEVO/T8ofUmXQeYvg9Q6KoudClgqoapclpiBGIe912Hdggu7EBHv27WHHju3s2bWLPJ9hbOwA+w9MMbF/gs5Mj6zRTgluun4TsaFyEog1gG8rqoP1yttxqUnQa3d55hUMtd5rJiE53lsqi9YiiGYkJoKokl9EvW5jYyZZFRX0eiiZDQM+ElOHu40GySJlr0yv1yXOC+NHahxWO39FIK+HnaWCeyv0TEHR6fLozn0cu/E4Cm+0c9WrqI+4gJhM1dGNUS0FAes1qhdhCmM8UoL1Fms0uCCWpjEUCN44QlGAK8mkkbALVcUOFLR8k1JymraFUJAJSubBMLlnC9/4+Ef5zNV/zwknnMbExBSbNp3ML//yi1i5ciUxRprNJhs3bqTZbDI+Ps6dd97JaaedVjd9zb8n/UGkH1gNIfDxj3+cCy+8UFNLV/Ew7OOm/vXZe977VNmRMboj3nffT3n2s5/N0qVLsWTknRxxgZnxkgc3P4CIsG3bNoaHhylDuaBj3OENu8CfF0hok+Sf6fs7RnkQMUaKXg7RqAUCKn0ZTSAGbQKzxtIrOsxMTzPTyxnbs4/pmX3MzHSYmhxnZgbyYorJyUlmZqaJPdi7fzedMuEKTvkbZSG0F7Xp5h2mejnNRgPEMjAwTGt4hEbT02o1GB4cYeioDQxnbdacM8zo4GK897SGhhEcKSGuK4+VVKYknoWN6hjnbRMhB2kiaV4aoypxXSlpuAyNbRZjBjCpUdThiC5iUTJbRkYwJFwDnGkQbYmjCaZHSQaS4UNETIYzgSXLj2KgkTFVCi3j2LX8Ec2MjugGouMpYSCaHQBx1gU9WEFKdcz627/6a95y+eVs2LCB3sRUfbyp6L3iK31pkjgsxJhjEngZQ8AbiJRkovUxEVUuMzFiGhYfm8mSwSKlxzejQiEJnMoM9PQbgKjg34d++618/epPsub4DRRFj5GRIUZGRlmzZk3N2RBRwRyACy+8kKuvvprR0VGOPfbYg+7HQoGgIouNj49z66238o53vGMObgLzz+59z0U0aIrM4juoAtjMzIy6/rUHmJqa4r777uOiiy7CWsuPf/xDvvLvXyZI5LxzzuWZz3k2I0PDrFixou4W/c8c1eeY865Gu4NBtUC//a2buf/+ezlwYJLp6Un27Bnjke3bKDqByekpMt9UnGu4ydHHHMvipUtoDAwyvHiUkUVDrFm5jvby1Sw+5iSOGRxhdNlKXCb4xiDGpf6m6CgxEDpqZWoaFL2cKIFWw2qQ82gjqIlIDBhpIzbQpEWZBZriabXVLIyQK5dJCqWd93WTYwVT+eYkdrREod1us3bd0XpENRpgYtQjbiwDUc8pAJSi3jHRBGycq8pfTWRrM4QmZXA1TlLGQl0G2y2Ma9DIrGqVZKYWRzrS47ACyEFVGNunKC5aCSkksmb5Sm7+7k2sW7eOkzedUpOPLAZvHWWIildYBQ11D1KPi+oYEWKGdRakrwnPaMoYi0hue2SZw8eS2EhWEOIV5fcNyij4dpNQKjI+vncrZ512HEdt3AhEsky/kJUrlyoRJ7nUVQu9LEsuuOACbrzxRr797W/zyle+8gmVLK215HnOVVddxRvf+EZ27txZ0+ArNqiCqAc/N7XK0en1yIxjcnqGMu8hRtXAvPdMTk9x2223MT4+zje+8Q1OOukkTjjhBF79mtfys5/dzzWf/xy3fv82HnnkkTmix0+X41v/qADJyj+o+k0MhUo6WMU2yqLADnmece5L6PmcJaNLGV68jJFFS2gPNHC+qbYfUftCgkSKnoKIZdTWiKqJTNBGzDJW2rlgglWA3giuUkcXwcccTQQTnmYjEgyZccq1sMpNwjhEcnw6oU1OzyTMLX0mMVRsUCBVS0DxLcXfDALOItGrE4ExRHF0TBcjgg2qAULlawQgEFKJVkxBdezTfw9JtiEQQo63DbCOMjqs6ei6LJsYBzF0wWb0ioJ+hf0jOQ4/n+3bPOf0c1ihSF/iwOiIaqIODPHII4+wbt06BU5NoMwSLVcsrkwK1gScN7gcbY4jIjYBTsyKzbiE1nurVpYWQ3DJac0oYQwRfMIbPEKIJcSMu279Hi96wfmUZUGwJS3bppd3cK7Jv//7FxGRekevwKoYI+vWrWNycpKhoaGDbsWhgsn999/P9u3bufTSSwkhsG/fvtR2H2i3hzEm4L26ryt+pKh5EXKkDAwtGmVmYoYHNz/Evr17KAqlJO/bt692rrvkkkvYsWMHY2NjfPWrX2X16tXs2bOH173udaxYsYIdO3Zwyy23AE+cLv/4Y6GdbLYjGjQzjVHohaDq4saDUbFiK5Zdu/ay+eEtPPDATzn7wgs57azn0jEoOCkabvIOBLq0TEb0ymoWZ2rRYme0nOl8ygCMTuVMZoN/RDvD6/KvTZYNSaFOX0dB+0oSsOLtUDV14pKmhklap0bFfxYk5Wng6m+sw0gSKyswRunqBsFHtWp1ouJDlT0zUYhGgf/SqjlUIaRKTjrWocHJ2qSHY4J6y5jUWmBLvGmqfk4Iiv0lbbIjzQU5YmXcqkMRtL6/fft21q9fz46Ht/HDH/6QteuOYu3atRgTcb6Bt1DgUPe5ZEoVLdFoebBE4beGJI8YQ/LF9BwdmQAAIABJREFUFZWYQ29yZpyS0dAJY1JeZER5KSYWmLJJjODJ2btjB5/63nf413+7hmOP3cCKRSvYtW83DzzwANZajj9uIy984QsZGRrGZZ7du3fziU/8L370o7t485vfTJnKaIfie/T/7jOf+QxvufzN9DrdBBZSu9f1eh3N3DqdVHmwFGWHvMzZumUrd951N73ODJaMR7ZvI89LTjhhI6eeeipvfOMbufPOO7nuuuvYvHkzmzZtYuMJx3PXD+7kX/7lX3jOc55Dr9fjU5/6FPfddx8XXnjhHHnF+dcJj1+KJd1dKv7JQiNhG3UmagzdmSk2/2wz3775u9x22230ugVlyFl/wgkce/w6br7+Jv7r71+JNQ0Gm5GiyLFoj5PYgswqZ8LZJkFmF6WJFbiOauWi2bAxWt6PZQCv/SX0EfRiUJC9/r4gBY6KiDzLt7BGVF3PKAgek9+RWin0VdwOLjOlha4kMm3fSPc2Ks9Du2IiJhg9ukjFdCW1cqiQljMuBa7+Cp+GgoxAQDVTRdSMvgiezIG3kvp2hFj2XVxfWftIjafkTFcRberfKxUVaz1nnXUWN998M0NDQ9x83bVceNH/qINMCHoj0h6QSl9Ws12xhHQO1WliCUScdXVp04l6vWTWEKJgjKbIKU7rM8Rggqj0nBiaEinFctxJp7FkuM3ObY/y/Ttu5fznnsO3bruFs55xGgKsWb+K97znPVz1lx9j597HeO9730uIPd761rcyNDREa0DPrgoC6k9JxzZJit29XsHM5D4ee2wHWx/aTLc7w9TUFDt3Pcbu/bvpdKaZGJ9icnqS7kyHsckDeON4eOsWjGvQbmR86u//kbPOeA6Lli7DGM1OJqam+cq/f4l3vvOd/PBHd3DcxhN43atezTkvPJfhocUcf9xGJqYm+dy/XcOBAweYmZlh7dq1rD1qDd1ut3bSW2jUgYRYz7GIUU0JoJJZ0BpGepyodWhRdtk7tp97772Xe+65m/t/9gAPPPQQ0QlLli3lmA3Hc/pZZ7H90UfZvmMHW7fvpDmwjEve9BY2nvBM8jLHBYc1TSSWZFlGr1t1R5tUXahkI6rDruu7/6H+O9bhjEHiXBOrWb6HGmbX1SC0zc2Y/qzF6XtS8XhQUF+qznDtUamb4PqCsFpJpkDtUhBzGqGC0QWn2FeG2ALnGlp56t+ERHvpiII4qAwmNOhJerkMmzIva4QY9WgYCGpET0EWHWWjSm2enqOrkcNokFDLHVd/GQ89vJlrr72W1Ws3YB0UscAGIc9z7rv7J1x77bW8/tLfIGukkpMV2s0Buq0mjBzF4tFBhpesYmB0iDyKUtzLEiuJEZg5nFRnzIpkpHL1zjS0Dyb5xvhk81elduqTkiGhJDphIGvTiw6JJWM7NrPrka2sPPloHr7lP9i/9WH2TE9x9OgIP73/Rww02mzfvp8r/+BKTjrlRAaawwwOD6VFZmcDqEmcD+8pi4DPIq99/aV0jGV4cBml77FyzTrWrjuawUWjLFo8yujIYlrtIRqtIUZGh7HGc/u3v8Vf/Ml7Wb50hHajyUR3hvUrV/KCs8/h2PXrOOOXnsXSFauJEojBsXfvbm74+o3cc/897Nq9nS/9+9e48soredOb3szAgDbOfeTPPswlv/Z/sOnkeXobylRj7sSqAjHaNuD6HooFSsoS9k+NsflnD/Ld22/ltm/fxtYtD7Ny9SpOfPaprFmzhkIi+x99jJ/ecy97t+9j+YajWH3CRk5/5hmc+IxnsHTlCjI/RK/o0hBPNCBO6ga2ILNkOcUZQt3rVE/ctOD6PXdme0ZS1WJea4FidOkohZnz+Dll+HSU0PeX+vV06H0wJtlIMu+5KWshCiURj60xQqzB2YzpqQkGBrR9woujqHtuIuCxXrMV/TYcZIIV7XI3pWbsNjaJ9CC6ZIKmKmjeOnIpaLk2vZBDcNxx04285kXPY/HipY+/sA9jPOkAojc0JsxCDZk3b9nMtddfx6o1R2MdyRsEbv/hnZx87PHs2rWLvY/tYsNGdXPLnME2HdJahFu0lNFFyxhetpxscBgoidFSoqmo9Q6b3LcqYMkYPdsWAo3EsjNGsw0RS0Ns2kfVgMc6IQZFsMVGGsZDKXSKLplr4ZqOu759HZNj+5kM0zBTMtgeIrYb5Psn+M2LX8Yxx25UXkLd/j/LxKzuS1EUZA3P//q7v6e1bAUnnXc+VjxZ6dQIWQKh6JHHEmeaiA9ICQ2ru31mVWx629bN3HPbHezZ+ajqZezZQbCRdjZEq2VZtmQxnYkZNp20kU0nn8HRxyhpb2Jigptuvg1rAhMTU2TNBjdcdz0f+MAHZgWgE8BZEQCrAFgURY3Ud7tT7HhkB9sf28XWh7aw47GduMwz1e1QWuiV2pMx0MgoupG8mAGTMTXdpTXcZu36Yzn19FM59vRn0Gy2kTxQ5glPsgm7arcIvTxV2woFLKvrSTyLOfKS8wiL/Ueu/ikcUWZotYnMl6gMUmJRHo6IqMIYVTObbj422lQpdFgba8sRPbJEiKbeROcf/YKUCVcxIAXOacZqjM7j6e4Mex7bTbvdYumy5bisQSgqfkoV/BzWlMzMdCg7PcoYKHo5xnnElgz/f+29e7RtV1Xm++tjjDnnWmu/zyM5ycnzhLyAAOEh8lYiKIKCASnQiy/qXlGrqKZlWe+mZVnV2r2ideteW8Pyqu2Wlg/UQnkWIqAQBEJAICEhIe+Td87Jee69HnOOMfr9o4+51t4nIZpUorbbMls7yTl777X2XHPO0UfvX/++r69uUA+GqAguL5i0FvQy2VXUAbokxKnypav/gje/7EVs7N71WJb63+h4fDyQQpWct3Gxm9U/DBKEk0ePc/fNt3HR2eexsbLKWnHtsole1jqrcGR1uGCAqheHqmcqLS41uCpBrgzNL74KoDixurKS0i+fm856vHoikUqaYi4kpJhxNOWG1kRRRstDJFcs1UNyTrzgW76DLtjDoTHRjIaMU2J61/0cOn6YC5wFS/vcGVG/IwOxHnHkk5+8hpOu4oWvehWqFet1YFp2tOAqNDXkEAlUtNIROkdMNui60oSrPOc+8xmc+4zLOPTAQ2wsL3Po/tv52Ef/jC9+4ir8LDJ62Tov/NbXcPD+g/zGH/w2N1/3VQ7edQcvfeGLuPKNb+CFL3opu9Z3kzRy07VfY31jY15y9kCzfY7EeHPGpz79Sb7w+Wu48dbrmE3B10ucdeAA5194Jmt719k19Nx64y1ce+31HDx4kMGg4ZJnXcYLXvYKnv2CF7B//xlI1eALmzg5qFxF7KYQPc2wYrgkpJkR3Lps0+1Pugm1esRVRAVNEeerOQlLCzEKTYWF3AePhHN+3ioH5mpWjxi/ggX2MRe55UwgkJ3JIrLYXNpYmKO58DhMrOjpcouXmqQdoQ9wydqwiyAkuDlVvLy/2uTE3JtmuYAQEecYHzmBppb2ZOKYKLtPO5vglZStrPIISSInj23y0LEHabRaeLBOpnjveWByF3vWz2BpYw1U7fPmyjAXiUCHMEKkM0nGI0gmnqjjcWEgD9NvlH/GGAmVh1lkfX2dYyeP8ZUvfombbv46b/zeN5GTdVeStiy7NTYlU/uAtm7OOlSXGcRA8kJWqCSSnM27NcGeJ6pZJ0IBurSAaL7MxM0Q0xh8IHVWuxuZx5GTDRvenE4IjXB8awuajE+C74rJTx2YnpgRdMbaOWdw6PO3001OUA9Xgb41WDoPJajkdsbWiRO87+Of4q0//U+ZHt+CQeL4bIDgyXFqjFuBmDKVtHSaCW6xG7bYvFMXFKJjaTTCxcxpp5/FW976A7zlbT/MffffzSff/yH+r//0H9Hc8uorXscrfuY1+LrioYP38qGP/znv+pVfYXb8OK9/0/dy1123oNrSxciho0f43Kc/zcF77+Xaa7/KQyePsxSGnLbvDC5+7jP5rpe8iOOHDvPVr1zDjV/6DB/76CHW1k/jsmc9m0ue81yu/MH/jd379pJQKhkwjlMqFVIUiB2p7ITZZ1KOiFdSmpI7h3gbjD6Z9d62nQ0hK6m3JDV9kyaUZDogXzoYrmBkcUxyNarg/aLz02MI/fNJMs2VYWqFo1H0U4gYJuCMaOZcjRTFq7WDATJJfSk9hEYMa3PSb5kOhxJ9yXbUNkCKuM7RNxMENCDaod6emy62hORotUXHsBYjwdmY12IJjGpmevKkCfIISJqZs96wdJoYsnnyGNJ4VoZr1mFxihANNNYBQk0bxwxcbXNlgoGv//OuuDuPJ4SW2Jsg91wDdcpDhw5z6y2388wLn16AIUfWtgQR80KtC/AptSkHtXRckkak74PnjHdS3M+sVHEZ694UJH3H5DetQSBJZ/Wj7+t3O5REjlYqdG03b4eqt50nNIEYW5rBgBgV7TL3bW0x6yA05ifiCjbmMFAvdpHKe97/qb/kze/4h0w2txgNKisLakGjIl5A05wbkLTPuiJRTVSVyDgJjGeRunKktiW6hKOmGQ5IUdl/+jlc+UP/K//Lj7ydOx+8lzu/eiNf/dwXuP+BB8gCZ+8/i1e8/nUMq2VOHj/Cl6+9iX/6r3+W5ZU1hqMlVvbsQkdrHLjwmawdupcHj5xglpTbbr6bO2+7hzPPO4eXftebeMMPn876rnWCb8jqQVpcGNBpBTphlqc4VWtF4uabSBCHJYwCOVOVMY9969MElaVLURjI3lkWaffQzcHyfldPWQg6A7FRG+ZoX8x1tgUPyrOQaPFl1KMXZz48bmE30f+84Wc7h36JqA3mEixj1IyKn2dw/UpXAZ+DGVX1R+n6WBcmkGSKxzaNkOHQsUO4LLReaNyIZjCYT/nLsZsHrGOH7ie6RC2B5Dp8qgoV3ZoP3iW6rBw/dJjmzAH97KL+8HRQRk/kmEilvHGnEBOfiON/qo27nZyykGob4LVrzx7OO+88vPd0XcfXbrzeJto7Z8Qwsdm2/cwLU9ZOUa0MbMpijte2ERhQJCbdd97cyDSbUlNL7Z6TzBWnwXm0oOCn1ssh2PgJXwXarRmhcaRC6oHO0vCuNWVCnHLx5S/gF3/lV/n5f/XTpJyQIkrLGkEdXdfxy7/x6zz3W1+N04rlwYCUIkhDlZU2OzItms2RzUvCaTWXYXtXtDOAaGRQgUhiWCvCAMFMkXGOIDUrw4D3yjOe9nQuu+DpTOMUGQzImxM++8lP8D/e8z4efPBBLjxwgDtu/Bpnn7nBX119Dce3jpOi8ryXvYznveIlPP+c13D+WRfQ1BWhVbraoQlShC4lUtpiabBMHeBYK0iXIU9t3z/F11XotVAZKK1O7TUv/fW3DFI043yF9vN5VOmB8X6ejbE5W0SGNBpR5+wPOzGQftPaLoprCIZV4Ejq8VlJEqnFGgCaBRHzU6l8IM6xLcVklx2VqulzpCo8jL5l2+FJkE2rItpsM+vJaKHuZ01oMkGo8wFyR7c1K+JqCwRruzZME+Y8uTybObWMpxGvmVQJTfYkn6kaG3nZzWak7AjOQRK2jh5lZffesjLt9yeXqTSBC1b3ux4stiz/iTweJxPV7fBJ2K4aNb2HkDphtLzEGfvPZPlry1xw4EJjBBbfR9RB60liWpce/e+HG2enNs7B+RIoApqLB2tOBch18+xCc6l/WQjONPXMS2v79ucH3qbf5ZYqBFJsCSK4ugRCVSrnCQGINdEL+y+9kKv+8tO85MUvss+cza7/8OGj/Nff+RNe+eYrmfg16qpi0s2ofIPTlrY1rwh1zrKQbMi6SqZxVfGBqObnltVa1mYck6mCWMkUA1JZlkSMODdgczKlCo6tcQvjRDVwvPBbr+Cbr3gNjhk333ATMlhhsGuNH33D93PBxRfZw52tlpciSR+PI20U8vHjuKqmrjyhrqiqmja2JmDsCs4v1kaMORGczBXHqbMNwa6vpfhJbQPwmDQhacZlR5dMOJdzxLsGCZF2nFkdNXSaSQl8FUgJBs6jBKbdDO9ruq4jBIdXpVNlNBhx/MQRRkvLQEC7KWHUkGOiCg3t5kmGaytszaZ0XYcvY0RimdynqhZYSpvVsAojNYp6VC2wp2Jm7UVKGSOG3znBaS4u7tsGnUkufjWBmGfk1NK1HbUTfAFuXVXjkDm3yImwNZ5AMjkGatKLjb27aZoBKsqJY8c5fvQQ2Tek2DKeCmvOAnM/8sH1SmXpSLkpHSd5woMHPI4A0js1PlIqZExUIEErU9505Rs5eNvtqMBgWM+l28aZsNm2IZp8OmpkgCdKAPoxmEYJzlr4CMq852+7hS+n4VCJKB6RArAlKW3cCi11qpRIbClNxjvF5URvnaFqD33lPSlntIyp8N5z+Uuv4Hff/SvsP+Mszj3nHHzw3Hrb7Xzw45/mih/+QWZjZRRqkqjtWnGKOGPM+jKq06uSStuxruoS6MKOnZwC0NqnEnO5TzNc1RQjJfuc0+nUFMgRQuXQlJAEUcDlKbMYOevAAX7kne80sWNMtG1Z7CnhrS/OeBIRUWoJzLwRk1LqSFvRzGuyqUwrH0C1FB4VIi2qgeBtULqUEWwm+HKkAnCGnBEfmE6m+NoCp/hA205BbIbNoApEbY2tqjM8gco7ZrPITCtSiIyGQ9rYsdx4Jtna6JUq2kXWV0370cXMaOTZ6jpGg5qgmfuPH2GPeG67+WscePqFaGfXi+TptFhpltXqy9p3CXDeBj25TB0zUjyQbWGWx6jPiLYvAic4NV6JFkp7FYVJOzHDZWltAPbAWymcbVi9iDUg2s3NUpYHSJl6dZ1Bs1TkCI619Q22traIsxbvDOjNGUIQy360Q1yw0jJJgY8U9FQN/RNzPOYAYp2StCN+7HSMgiyZSmsmbHHG2efw8tqmjx04cMC6NVJGAoogrgVGBOkt7LPlCwriHDq/Y5ZF9DoEKR6Q8/ZaWux+81q6r0e9teIMavVo7lB14CO5TEw3gZK5pcWcaXAkV5O7iPPmDPXGd/wEf3HVVazceIvZzi0v8+LveStdm80BTcySTjGD6SxQldvmCv/AiyMXRexCmt+zGi1Q5sLmncWOkcCs7OAxdeaLIv1sEiN4oWoGZb1+SPNcPDf/4xeZYgiBHBUJvT4kkcVAP+cCVJk8MVCTmKnqaq6nEakJTpi0Htd42jZRByFGNbZtyqjLVFXFtJ1Rh4pJnEGVOXlybF4qEsAFjhx9gF0rpzEbCycfvI8TxwLj4yeJOuPcAxdz2003cMa+s7nv/nvIrfDMyy7mV9/9br7vHe9gazLl3oN3Qsp8+jNX8ZKXvpwTJ07w/t/5HV70rS9n2ma+7fWv4eqrPsn6yi4+8J7f55/8wr9j157T6RLYqOrEwPm56K9v0Odg2ayQ0exRj9kLimF0MWfUOTpN1Gr4jSssUhsJ4Qz3CsUMWYQ4w7g1oUGzMhqultWTCy3fgnvbpbI5W2BbWR1aNiogTvG+Nme02YwOcDnNvYj7ERCaIqGq6bLde5eUvzdyfjvJnkdQkrlCZTcbezUlYfmaOmVt9x6Wl1ZBMqlnB0oqaV7G5YS4RHAVSZwxHEswovTdEYdzaimaWyzAfoGYwqBgHFqRXBE7kazVJr4QpwqEJzbkCrFHpb+8qongAskFkBZCTcoVGibUOuCSV1xB7So2p1uGW9QejQ2iCY0OmIFLVOJJpV1oAi8Dc1WL0VEBkqwT4ADbgVzlCepoNZqGoYv4YGmu0avT3APFJZA6WPYh1k5MGnFi3+9Hh6oKzju8cws3dYlUUptCIidCqGnbTIxTAgFCETwGYTKbzr1FYu6ACkIix0xVDK3FOyg7tagJCYdNzcluysEvX8edd9/JK1/9Sr524/U85xnPYZpb/sv//i7e8kNv46Lzn8dH3v8HnPO0ZzIMmS9+7ou8/LXfzsf++x+xdeIk9eoGb33Tm/kvv/J/ctPVn+OG51/OAw/ez9F7jzNar8nHjnLVxz/OxedfwO7T1rj1hpu5847beO2Vr+Xg177M7JyL2Dz6EN1kEzAH+NQDvMlaxIrhd+K0ALClResTsTObTYAYW3PXyx2Vb6x1koWkad6ZsyHXBXfwClFJuSWEXEqnAVVdSj1XlESa0Qwp9WCuh9xnOcGAWzU/1cp5xgguCdnJ3BbR/igqZQ5zydSd30l2eyKPx8xvtUE6xgPZrsbtJf1BbJ7tHbfexFe+8GXazTFNVVs0VyVg0moh4NXmyGRnw3iidMbp8Ns6OlpyyqIjcBJImsswYfu9tjtW2FhMT+c6E1+laNlbiohu4bHWbMWUIK0tOg/iDEkXUev709FEgTxkQEPtlIYBPkNNTc6RgfeshCFpouRuXEDXKcOwoPfb8Kz+5lqN2oQlNIBjCRFhOFihHq7jEKZbY47d9xBuMGBQryHZ46qGQbPM6uoI7xuCH9AMPQO/RBgNqJxnNKqpKo9LwtpwhdFyQ1MFRDyDpiLUMBjUdF1iOBziK8eoHhCWBwQnDOohS02N1I7dG+uEpYbVpSHNIFAPPLvW11mqR9SVY3UwIATH+mjE0nC5mDYZthFCIDAgaaRxNbfe+jXe/Qv/B5PxCUbNgP/x3g+z4pdppWM4qNg8cZTp8SlTr+Tk2bV3F1+++kuceeBpPHjHnUxi4uh9h1leWWHsIwM/QKuGtpvgcdSrQ8469xyyV/bv240fBG77+t0k37FSV8ymylduupXhaJW10/ex/5wL0SyE4BmFCqddKb1sszKzomCBRAzH0DIv2ZVsWKhJqnhXk7oWn1vDTebdFwveRkYNcyYqUAbKV3gR6oJ7ZY1mGF66VcFnvI9mLRCcdcDIuPJQuRyg8pBtEzCaTJwvZU1WErkYaDP47MtkxyeHyv64eCB9T0tF59gBgBmA2eI+cewkqspdd93F6Wfso6lqUBu16EoWklSppLQ3y9+jWqcsR2O8eldBFsRXILncXCVnsf46NV5btBgwe+fImqkcdJLwLiC6nZhk5LXAgghmQK7tHiZmGpClxjFD1RvVI2ZziMq5sA0rpnHCoF4jz1qqypwpEw4fMCNozdAb5JYjU5zcY2Rr6wQfev9vk+KMzRMnyDHx0EMP8aIrruDIfYe5/9A9vOMnf4Y0TUge2fmGzHTiEGmRaLvNZKpUKITEycmY7CpcjlRNjVDRzaYcf+AhHjp6hP1nnEm1MmQgQ+6/524euP1env7cyzg5m3HLDdcT8Azrio997GO85fu/j6uv+ksSibP3n8fS+jIf++P38Z1vfTP333I39xy6k69+9sv8s1/8RWazLVAzlEIzMbfcfcftvPQFl/OnH/owp6/vZeW803jo5CHOmOxn2Qe6bsbnPvcZnveiFyEeth64i612i5e/5AVcd911dKmlbhpGleeD//2DPP+Fz+eLX7yGOIm0WzM+++mruPyyHyNLzYOHj3L2hRdxybMv5YzTd3N8uIaXjite+Sqe+aznsPXQMa774pd49vMvZzrepHE16h1eoS3q8SyUVqd1M5wElESQqmQYzHeHLEVFW4SgLgvZU1SvxoQWMfat+IDXgKoHSXRdh+oyTpSQjZiGOsiRFB2OChek8F1M45NL/emdgy7hvBq+l7v5sy3ePGTBQRAb2tYPjN9mO/BEHo9rMl2PHMm2/xu6n210Q8qoZG649jq2trY4//zzOeucs9l/xpmEutqRTmWs5OsjrHk89NyOANEVkoyh8z0ZyInSxUgl4IOSyQboITasynuq7AqQWmi+pRVsIHAsZY1AStZuE2uT5mz1piZ7KFwSCJ7cRaDFe2vLNoMh7XiTwagxs2Qv5XOEec1ppkbbdqHUkZ3Q5Y6jhx/g377zJ3j6sy5nz549LK8uEUPgxLGjfOqTf8ZstkWbAsF1TOOU4Gs+/Ee/w6u/+7vpgnDVh/+MS859GsfaKbOjR7nuput59euupI0n6Y6NufH2G+lOBF72nS/k/b/5e9Aol1x8GYPVVf7kfX/ED3zfD/Cpj/w5n/jI+/n+H/8n/NrP/ks2zjyP7//Rt/OqV76C4WiZ9//B77N7zx4ePPc81s44jSOH7+OhYyd5/x//Fmeccy5ZW3t4dWFY5LzHacB1cPCBB9g6dpwjwxFnD9esVV7Br/3mb/Gvf/5d3PPgvbzvj9/LvjPP4sY77uFNb/oH/Oy/+jf8zM/+G5714pdw5I6D5BoOXPJMtMvs2TiNj37iA1z2jG/iR//RT5Nd5Fu//buYTDdJrubVr3kD4zRluRpx+8FDPPMFz+bBkw9xyUueTw2kmKmaxrI7OlOfFJXyQvrviut/QjSQtLPnoVcE43FZqYO1aoUGLSZZrmAnIh7BkfsuYxAjmPkOp0qXMgO1sSYhO8uYSUjqUBxOaqrVpvBl7DAOTUdUW3A2tUBKhhTK4CprSWfJODUZRyhd0yfjeOx5zTbdgRabtO0W/OIdeEflA9kJjQ82b8UboaNLad5O6t/KzUUOYfEeZQYHJT1MmuddFEcPIArO6RzTyAUkXVgK9mpMV1qMhfYMBkaSi6+Cm9PrAVyw6eZGXW7NjzN15rPgjccxrBy522Qw9Gg3pQ7MOxbW8jPtzjxYFjNeV1eIeJrQt27hF//f/8o//w//kV949//D23/8J1gZLnPkrnsgZZAO3DJHH7ifY4ce5JYbrufEscN84Ld+n8MH72CwFPjQ7/03amDoK/aevof9+/fz6U98lO9729t524+8hY/84Xt52qUX84pvex1Hjp/gvIsv5vAddzNtx6Q0Y7Y5JlSJ9b1nMlpd5/6v386//Kl/wZZucdEFl+IDjDY2OHr0OJPNLc7eu4trr7mW/eeex+Z4q2hKijrZZzRayuyCJyGcOH6cFIVWE6ur60xPTvjm5zyXO++6A2kdZx04wMb6aVz2rOfywOYW7/zJf8GDWzNuuOEmvKuQXHH9V77KHbfcTr17hVd9+xt45rOeQcpTlpcHhEo4fWODrUOHufgZF3L43ru57ZYbSbMtbvzidWw++AD3XHsTd95zN+IgSAU+I7m3+H+8AAAgAElEQVS0/XPcwRPSLGVqnPGTnATQ7XttNuPvLESCZSylBDJhnieQiKLUBY/wzkp1Hz1JoGsNewnO4SszAOqfxeQySS2zyNKyw8BandkeuIDkUoJ7LEuhPPOyOE/JnvzXjyx+3MdjDiA7OB/ojq/3nIuUrLYMDrLzNJWfB5jK2+IU8UjKJFFQu4GhzK5diKOKOYwu6tC+A6PiiruVtWdd6abMg4wr7mSu78YUnEMxkJZstvnlwQni531/EwxWUGwBchny00+iE1FiLIg3qbQ7e3zICFRKonLVHOg10yChchUBa8eaEzccuvMgr33RC2jHW/zub/w6P/H2H+Kaqz9L2yldaf994D2/zx1f+zKSMmfu3sOH3vsHpNzhhyvcddPN3HTbLVRVw2B5hXp5RKgbTk5bjk1n7DtzP8vra7QxUS+tkNuON37PlRA7shee9ZzLyEmYxo7N4yf4wlc+wwu++bkEqTn/0os4cvddjFZG5gdbNXz6s5/hyjd/L107ZTRcRlUMXBRPFyPOSZnlWpPaGRFl98YSk+kJuzc15NhywdPOZW19RBOnVI1n88hhJM541jdfTnvwAS4991xuvO7rHPz61/ngH7yHa/7yk1x88cX85q/+Mn/+4Q9y9pl7+e1f/03+/E8/zsc+/H4+8oE/ZjqZcNvNN/LeP/xdbr/+Bq65+vOcfOgIh48c4qqPf5wUE23qqFxtzw6C9vwNFmCjdxWop3dKzznPNxkp99TjbSvUhRUAOMs/1YZCRVFEM6EyyUKUhMtKN56RUkR9IGomhJrUTsp0AROBNqEiJVs7/fnFpMTckUlkgeykeH/0WYY5pmmOSPAk19LbFjwZx+Oksm+LO9sCioGZUFVDWzDimU5PsnVyk/r8xlipKdk0r5yR0KdnrrBRO5uLm1vU12hMqDcjGB+k9Mv7zkJadF9yX0dZyeKwVpt3PW+kXFQt1rfOXKhs5qmVNipS2JQ9y3BmqahP1K4iZ932Xq6Q6To0e5xAFIcrjNigzDkUfcXXZ0VZp2jKtO2M8dbx0jky1XHbWiD9xz/5U9x6w9c5OjlCd7LDrSobp+1ieW0v3geObo654jWvZVB59u7bT71U0bYTqqaIy1olziKDyhEnmYsuuZQvfPYqdu85jX179nPHzTfzwQ9+gH/+C/+epvo8991zP8fuO8yrXv96rv3Clznz7DP46me/yPjkCdQpKdSEnJnmzNLaHp777Odz18YuhsvrtFuxtKNtF6zrmrYfdZkSDz7wAJKVXHkuOPtC9p29H3WeP37ve0g+8JynX8rR+4/w8td9O9d96cuMjh7maZdcyic++aew2nDGRWeyUg84ePAg+889h1u/fgNXfPt3su+0czkx3uS13/O9kCcsLa/z/M0T+KUl3vjWH+S7rnwz40nH8178Ytb2btj8YqnQ4HAp0kp5RnBWkmZgXnYau9mAfC1/d2Q17KN3BzOsRHCSSPQLPTG34O1ZywKDpSVEDtt7R2U8OUl1bEC1uyEniGnCieMniUnJLliboampnC9qXltox44f7rWFCMKwHmxbe2ZxIdlmV6sq0gWSdE9aCfM4A0hvK0N/zenNgjyeWdziogufzic/eRV18KyvrzObTMk5U4dQ3MLsgtSuKvoSIxEhZVB1SohmaoHkEjka9iFZCrFJaelvUGlZaQcaSJLx6snkuZtTf45gPBWX+wtbeCSF3mzZh90ELxnfZxkOnKtsN5q/f1F0qEPVWemVIur8PDvrdRbGUFQ0mbAspZaKgAfe899+G4lFfJWMwJW8fZbltSEz73jL234E11R8/CPv5/SzzuRt7/wp/vDd7+KrN1zPj7zzp4HMl6++hluu/wpdm6gGDZ+96i+49lNf4FXf+1pm00xbdsTR+pDXfs+VZC9823d8B4dObnHgGZdw4JLz+JaXX4FsrPIDPx44PB7z2iv/Ad/2hjdQhdpavbEjdrDvvHNxLnDu05+FQcNCHYy0NxjVTCeRThPnnn8O7WTKP/5nP8PJccfS0ojxyaO87R/+GPsOHCCeOMFk1rK2sc5b3/E0yLA0WOLfvus/gS/lZ9PwtMueQwo1aOKcA5cCDieZcy8aMp1MSNOWwWiJpgnUZy9TqdICKc5owoCokbbrsIZtC3iCs43AZPCl7aqWESPeykcN87GTIkbOytI73/mi3Uo0WZk5MSJZNpZ0dpFGhQ7LOqpBIE8w9W8Wjhw+zNax3mw8oakzpa8GnBPqQWOU+BIsUkrMNk8Yb0cTLguj9Q2gmHGVbqRKhzAkkW2Im7rS4Xjij8fehREWGAY6J5SpKrnNaCUECcxCx9v/0Y+xeegI9913H3VdI2IYSCWuRNUBImKO2YCThqRaBFnOMkhm+FAbyp1NumwT7DIVsbAii27Ce0sdNZAl4sjzRS+a5zViyCMzKxLrxqRiHiM5IS6apsb7QnV2c6A4Zhtn4dU6QoqQxayFqiLuMyl5tNeV+ai2HQmEwqxNHXUzZLC6SgbuvuMWvumKVzIdT3jm5c9j967T+fq111MFRwyO5ZQYayRsec66+GmMjyfa2PLcb7mCA6efz+ScCxgNGw5c/HSG63tpGuXCSy/FV4EXftNLiQg/+M5LSZppsw16vuQ5L8arpcrnxMBk2prh73DAoIXj4y0a52hJ5M7RaVcyJKOyb02Uypslg5EDS6pPom0zTTXihd/yMm7+q+v47jd/H+OZ6ZPGJ45DqDn97HOtS9WssLYsdFmpstJJYtJObCBY8eGI3cRwrtiSSVQKVl4q07iFiic0xnGZdlvUvmErzaxzlzKtzNCYCCWjgIacbWKhSJ6DlIgNeRKbxoWU5aHiLYsqz59TY0AnTAGrPbksK4gneUcXhRAaUgYvFYnM2sZu7t06iM8RV9VUTtHcFoa1M36POCR1uGaJHO09554lJNb3ns6RB+4jzUAqz6ge2DMVM5ZfJ2xWXUeVlKQZ1SdvsNRj54HQO2CXL5TzEnVGn83mnFRVDamLDAY1+/btY2Njw8CkworsmX85t3OeBBotuJRSJWybYKfZgCzpw4YXnKtR+hS6lzoHYi5muVggsjrW4ZLHpcpqSMmmVCzMWkfGSV44nxXeiojtNGZ1V8hEtPTKUt8T5rQoS1WMiViUpBkhaa8uNSRfnaPyDb62a/AL//ndvPb1r+fqq/6S3HbcefBW7jh4Oy4YR0NTwmcjiX3Hd7+ZqbY45zjvwKXIygpSgwvK0sYuNM6YTKdorphujYm5mAUlJc06qmQqUYlTIOANKcY7u+ZePZ0kk7mLkJLigjAfhpVtDn0l3vCebZZ+fQfN+4ppu0Volrno8suQMCSlmRG2fENv5JOzSRx8jHiU2JkC1shTieR0vrj7YjdQFwuACjxkalsk2RnWoIGYZtaLi8Wsuut5GlZxRy3MTSlt/z5JVWcizLLYEqaLktwzfy1gJldyT3VF2GH3FLVJdb1PSers2UwpmXCvblgeruNc6RwmB1KT1ajnOUVqn9GqYnV1FQ0NvrLQEbOieIbNMqu790JTs7Z3NyLeWNYl+yEXp0CpySrm+NaTN5+E43FpYcB25X53tTautcHw5haWVQulOuCcaVt6uq0U8ZygoDWBoqgsBrSQ8SHSqeAlmTGLZLK0uBRwYplAJ7mke5mczEskasa7TIqhEOBLlqTlEVT7FClZGigpEaQz/EM8TgKNS6CW7IrrKcLm8UB2iLMUNGq2Xbu0iKF0kFLfm7fzNlp+Uat2mIivGhKTsrF3H7PZjF/7z/83Z+0/HaRiaX2Zyy5/Hpdc9lzQxKzL1HVt09YFBnVD22ZcBq+JYe2J0dp34pRQVbTjMaOlAWlrxmBlidlkSqgCpDEhm76nkwwpEYGGipTLeMWUqZ1nlhKNr+lSS+jLCWeT4bXQ1l1wRVtjC6eWijZ1NM2QyWzCaLTG5okpyyuNidkcpGxCxxQU2jESHKJCqCJmLp2L1N4MZHJs5xhSIpIiuMbIZyIRCpcHMb/cnMCLAfROLRBUPTheNgjB27OV1FzHNGBiAyU5bAFmK0dD7ub3N2cleCG6CtSsGKz8NSpAKs+EAfoY6CqZ4AMxZvactpt7p1uQPS0ttRNEbbB7rB2IsL6xynC0CrkjZYcXG8St2ciSy6MlmjOXCN7MvbyrDFszyK3449jnjLJgaz8Zx2MOIPMBxLKzpopdnpNeJHh0YjqIOJvMfSsXsu5iYx9ApCXr0MRdmsBlQvakkgFYhIrWAgUkGM095oSXGsETsrPFkBeYQ87mRjbfwbbpQmIGNON8IIjhG1mUMCe4CV6i+UJkC3UJE3qrmHu8eqFKZV6vIaFAr0c5tZu07fCZpWYFzY5LLnwWn//6bTAc8NEv/BVdl2nbluXdSyieyWSMk4CvIplEXQ/IbVeo8Z5ceAyxU1OFuuKYlgRfWUovdU03i3N1sqgFwNxl/KCi6xIDP6RNBj7317xLkaoED1ED6bTojEQE7QzJ64PrHMSTjPOebtYyCDXt9CSD4aDc/0TKxQbQCSSlchWzrATvyUlJMeLFmdjOB8gtwXvaaO5aogWeSP0sOAvkYOeY+jvuzFTbB4/PlNJFcc7PNzhLC4VUMIJ5RuWEnFpru6ZkStv+2XVSMtop4qviEE/JOItHq2YkZbJamZJKBy44h/iGM885hxPjo6SH1AzCveCawLBaYm3Xiq2b6JAmUMWKSRxTSUCDucPHHBBJzBIEtVa55grnlJYIWchpRhTFxZaUe7vKvwdjHfrrPo9nZY3UdU2Mpm/JWtSFzMgsFvTcgi5brzrgqcUX3UFpd7qA5g6yI7ji3eBAnDOWIH1XI0Bp02accUkK6SvlTOU8HR2VFFm5SGm1GqruXU3KLTkEfFa8D9aiFikmRoEoiZK8zJH33hAnKEYeKulj/9l0RyBZGP/ODXw1EWMmxxl52LA1GzMIjlk7I0WlHgTaLtPFMUJN189SJJVFrPhczTU0qYiwzHejIttwkWIebKxe1B4qLZiMJjM4Sl2Hdw1tKn6b2NySfrEsXLrUwDl7VNHCFtay4HqWtGEgoElLx63MLu4HSsnCQzTnbIEhJ+rKAhku4TF1cqgb67Q5C2Z2bU1b5XPNfLHGhPQ2As6Gp1u3S3G10ffrIIgmuiyEnFBndgPqii2FOiildHCFDS397GUzfXbFQFk1z9mps9QZHubVSGIwl1lk0fnMZ59DyRCTZRQ4Vtb2MFxaNylNTIQQrOxHaVUIYlhSpZnKeypxxdaipnaJqqoZ585axqK4UBNjS53rOeUhdhOkgioMWJhMP9YV/+jH4/BEBZ1fLOvFLC0tcejwA5xx9umgjhhL+zQWA+Q5sauQdQoomUVKfWn0XSgda7UavNOO2jXF+8N2N3BFLi2Q7balMpRYEyiRKkS6aO04w1IgS8SXkqKuTcUoPlMBUcx0pnfPdpQHQRYCPVPLluAljiQYel8+W5/5PFKqeKo7eM4R8RVpNqVuGmJsqaplQlVS/C4S3IAokaTmN4EGu06F59IHDfPVKAF7W9DqncYXRDo7lyS+tCLLAxXAZQMF+46EBVKsRClSACUXNq8rz8C2SXdZrQzJOzOuXmk8v/9aHqDcY1YmMUi5RSTMQUt7zgrNubzePktGCmaT1YKheLfAo7yfl6jOedNCORvMJMWDJSo4jZhPh6LOm/wiVEhSTLSdwXkcmTJfAZWAkMgiJG0IlOl3JeDO59+WzyiSyLFoet2CKOnJdFGpXSzBq8KHCvFWotfeMY5WuolmYmcBeTw7SYyZ6XRM6jLjzeP23CUs+Gali1MCidpHvG9IkqllwGoeMxgNeaKzD+Cxu7LPLeAoPAocqYscPnaEX/qlX+SbX/JiUjagKsZInMzoZjNin4GIPTyDukJX1lja2MfK+gZru/cwXGmIeKuxxQKDS2Yu1H9067gXST8DrHGsRM2k3OKlI7iKNpuPZR0qhHa+wJOqBZJKIfGwBb/DZWtb7Thf/IUi3H8tFJJOH2CkqDjFQ25tNogUervVyRgrlUxWKTtzASFFitq14GFJ5uxGTX1AMKvGnhjUL9L+vGM2r9FeWtCzene4eCmGZajQy/nnLW7sM6WSFaLGqu0XaiYVbdLifed/1FqUjn70Y69B0vmohO3nkCThtSImh5MWFyxLtEHtbq4y7S+IaMHg8iKL6b/e37N5MEfNWk1slIN4N7dCtKAI2WUqX5sZkpgFQ6KMpPRFlJYd+JoUbaC5F0fsg6kTtHVUwxrviiuetFS+JrallO+UyWxMjJnZdIuTx48yHo8ZH98iaksct0zGLY6OtpvgshJKR299ZZWqhWYlUFcjBnXDaGnAcNiwZ2MXgybgq5qVtV30lg3mjJSROhCzmYsrM0SGj2WZ/42PxzcXZg4qLmqqfgzku375lzjrvHNJXcQNA93mjEock9mUlIoxDZnRaJk4GrGy+yyW15ZZ27OL0dIKqokZkSov0xEJ6sklk8i5nXtXGvkszRfQwoHbo5KoBMRFRM3lG3VzlmrPA+EbCIy2B43t5cf2Q8WDTggygmzDka1VFpi3ptieBWx77fx9+wUm217jdry+L0dMNuAQIklSWaTM8Yf+71Y6JvOnKAFb3eIcPDYG1KaiRbsO7tSFbWbBWTs8Ya6UNsShKiSq7TNR+rZ8cSd3veepDaXOIRSFqsN7IXuzjxSaIknAcCmJJbhlcswLcDZapqAkkpqsoRK7j7Tmm+ElAOanq3lG5Ww4dqsGoE61o3KNlVkx4gWyaxgieAdbCCEk83EprFPVBG1mLB3d5ozp1jHaLjOenKTdmrAZt+iOz/CambUT26C6juDMKrERYVDXrCwtk1FWVlZYGo4YLQ/Zs76Hqgns3jgNX5XrWGws+ru7mD+0MAPSU54Ov0jULFu2grMP4fNXWmKlc03WE3U8jgwkzlumfZty8fEyDxx6kN//vT8iNIHV3RukmTI+sUnKY7xrQCztHlQBWV2n3nUWu9eWGW1ssLy6RMY8P2LyOGdTuwBc752qCc2O4PoRhEYwU7MkBvp5opHgSks4L86vbzX3WUN/bC8z+pbddjyjD1QJcylLRLPL7yn0Yt2afmaOzQ2x1ueO8qXc1D4j6n9P70uvajR/28FlR4Yxv2nl+zkzH0K07Q7tKGcWk9X8jkyg93DpbQhztk5afy4qQOFT9U5pfXCz+98HNvutSfMCYLc7Zj8D0J9PKQ/6TAEpCz+Bd5mI2OeJVhuLFyrXzPkZIQTEZbqcynmW8iEmptMpmydO0oVEHI9ptzpTvRZby6gTchvxIoQggCepMqwqcozQtsU53QaaVVWFc7AyWAZpca6mqe3rw8ESzbBmaTAgjAaEbZILC56enBISep8aZbuWZo4v6WKzyiw2BZSCN/ltYKNdf5ONWukuLKYClJfN79LccnT+gLODNf5EHY9zMt28CbttLsqiTTvrpvzZn32cL/zVX3HBBRfSTWeIUyaTic2GVRgNG+LyEqu7z2VpdYmN3bsYLK/g8EzpkK5illsaV1tr1nvIM+vb09nidAmTyy763F4CyUUrfbaJ2RY7bHm91GSNOAnzzGTnIl0Ekx2mvXiympiJWEqU1kGjBT/oeSUliBSptb2Xn6fu5vid5iVEf2WNg1z6+HSAMXdNOm7vm5jhZIit8Ip+dEH/+SJtCWyFd6JmgSBSYRPTyvCjMqEta68gFUPwS2bSzz/pouLLYCw7XxuYlHRGYEgbZ+V3JYIfkbINK1fp8Azxwa4NAZIKUafUWjHNkCdKzic5dnyTLk5pNzd56MRhlhhw7MRD0AltO6XNM6ps2IiLkfXRMl3M7FpeR2ulCcLaxgreDdizZw+7VtcITc3y6siCCFLkChXJFdd2MqKhmA3vzKjt731G2f8b68CIXbdobBP6YKkY0JlRvLMgYnNryuIvOFBme5lsr5+vJbDMuA8M/fqaf49t59NbKj7x3ZW/6fG4/EBcCW8ikT5l1+Jf6r1n4Ee8/nXfze7TdvPnH/0Ldu9eo80e7wNkJQfswWcBgOaeaEXCRM7mBuXF6u6skSr0Fso2EwbAyCe5oMulk2/GDuYViSerzawVU9JBcUfPYuSxPm44Z27tfWaj2K8JRbsDIGrjMm30YMJrbe1ojXMgUaWAjb2GwTnbadUeKE9xBUeptSIVRaexXU1DZKpQUzvjDUDMCkmVRIXEMmxJzCYxFUGi5rYELmPDzkc5ijmsSSrnXVl+YLyahKiSHSbFF0d2VnbUIdAEJToIUuOIZAmmRO0ck+mUFMdsjY8xHc+YHD1EnClxtsXmdEKdAuPZ2DLJnKiyEAaBJWnIFewejvADx7AZMagqdq1u8E1nXor3Det7dtNUZZreo62PAo73izxpxlOXMisYQJlLRwo1rUi/82kqu3Wy7pMD7e/1vExfZF9GfVJQT5BomUUB9wVM8tCDv35nwWEmReX7yjx49NncIjiUdjzbvrS9hinP+jaY+ZEvS18CnZJdP5HH45pMl7QM+C2UXgPjoP8gfQr80he+hPvuvo8vfP4azrn4acRZW9KvYGCgG+CSqRqdExAblxlyKIPgA2gkWBu+lAgLtzJgnp7DIoOQvo2HSQA0lV2YhS+C8SJ0R1q3vYsx16+w7ef7EkD7gFlGG/gy86QkEikraItUHuNGQ0qzxU1UC1TZK6lq6RR8UpIXExqioI7kzS/Tyh0T73lXWe0vuYj1KguOsd9la6aaCKlGguKcEbsG3ohVMTtShtQJrbQwTYw3j9O2J5i1JxgfHzObTZicmNo1mWzStglHhyYjsVVe8RoZrCyxUq/gmwFLrmLXyoj1s87C1xW713YzXGkI1PMyaeeRtz3Qi93/VFVs/zyVV+AQG8hVnjHJ1v61XnJxfqO/r2H+DtsD0NyHdv6fsouX50geFq3cw//fv79s/7r9vV/Y8mjLa8fr3I7ncMePnPJ129Qefo12/Ez/3BZtV9+7fTLIZI9rNu72aLbz/9vSMWxSXc6Za67+PB/9xMfYt2+fLTTvWGmGsLrGaG0vK7v2srprmdHSyo7g0CP+wLwLsL2kmHMvinmslmzGnM4M57Ddudf8+oUzU1Yzczmli6FzT0rZ8TBv/3f/symZAG6hEDYw1HsrJbrSMdk5/sIQ/H6sQU8688Hadak422uhoMcIcbxJq5EYI7PJFt1sSkoGUrjOQY5EjdROcN7OKYh1BiRXKC0xWXYy8AZcDitb2E1lrNRhFcjBsbK0zupoiVDXqFdqH2xhiRlYptSBZJxrrAQsQw8s+PcDkrQwSaOxMilT3sC8P0UIaqI0t6NQf2KOx1TunwJCPurr+qTllCVzapl8atv+1J/pTQROfY9TM4Qd1If5s9hnLX8/jseFgfx1Rx/1cmGf+mC08p/7uZ/jjP1n0mlmtWnoVoasrp3P7r3rjPZusDxaI+c4X/AZm9hmLbMSnNSYopVf8BAWU8WkfL9Y9Dsh4JklQwN7inHSFjTgnRHPxKWdnZCeXVo+Sw88Qt/pWLRlnQzwNUAqm6jpR9pZJKcZ00lm1h1jtjXm+NEjxFlkvLlF7DrGkwkjyeYLgpVKo0EAH1gZLOO9Z7muWVoZgCpLy8vsXlljZW2V5eWRlUB+QJJM8BY4NWH6iSTWuVBTgqq31mXKXSGXmeDKgqOl+1rs4bSk2UlzceF/+EKwjaIrXZm04+uLtP0RMIQn4OGP6CIAbc/1nySg8KnjGx9PSgBZHAtwR+m4954H+Xf//ue57NmXM6gdfmWd5dPPZmN1g419p1Evj4i5K1R46Jy1npyHjGkvEqZDgUDKY0KobWfLGaczchiVkQqKpFjKK8uOYpmL2+gI541DgBdaFWqxYVVtFHxpIbZpRjedMY1jto4eI2pmfOwEs8mM1I1ppx3EDmYzpj4zDDWDUOEdrNfLuIGy0gyp6gGj5SG71zeoBxW71vYyGDXUdX3KbubKotiWYtvFKyS20okSsRR+W+28fZr9oqTrQc+H177b19rD1p2yWJx/g3v7+L7/+I7+XLe3KJ86/u6OJzmALA7DMBJHjx3iT973IaYnjjE873yapb3s2Vhn9+7dDNbWwCmzBA01NELsFF/3KS9EMZt9SZlOgemY6WSTrpshWdjamjKZTAiqzHLHUjAqtLaRatBQVZ7UtdQS6OhwUUgpElQIlfFNmtpR1zVOAsE5ahdYWjET3PX1VepBTXA1uakYZE8SRegQaaytKLGkqG6O02+/EH1p9UjgVsIChS+LQ8q81d6DAqxeL/lQeU8KoN2XYz3m8CiL+FEiyKndtYcff3eov/32p4LH35fjSQ0gO3dAJaWCk2jii1/+Eh/7zNWcftpZhOVVmqURwTnGk2OkrQlb4xmbWycYSkBypqKjamoIiZXRCk29TIoTdi2vsjpcYrg6QsOAPaedzq61ZURAQsDFCphBse+31eaxBVC6SBoNPqcfYdxnTWDu3H23iUUL+RR0PiXzD0kCITuyY+5tInPA0O/IGnZerHJ6D1ucO//dI+sPu9bsfMtvhLj39+SR75fOeSzbMa3t3+95Jo8cRL5RmfLEBRzVEkgf8Vo9dfxtH09KALE2mtuxwy1S4rLwNPC1W2/k2OFNRhUMh0NWV9dZ37WHLDNGw9VtixVOZWeeinwv3tfcnZzWZNn5ei18BxWzLUSKw5nT0lFKaM42zJjGfn8xD9r+exZBpgxZ2n4+yjZeAT0FcNs5Lnr+28FV6X+Wxc8vmIj9z53KV9jGL5gvqhIktpOQTs0wHq2d1/+eU3kI23/kUcC+h9+fJ3iR989Sf9Gewj3+To8nLQP5hg/ptoXQ78Z9ZyRrLgSnPl23vy1OlkVqXl73SLtx/3seRsR5pAfuGz6Af/2D/7C37FH6b/SWbEfT5WFf31HOPNKbbCcTPYxY9Cjn+Tj7/39dqfBo2czfxvFUKfN3fzzJJczOxfLIbalHWiw9E++x7V6n7sTz93rE93jkrz9RG9rDPnt533kruGQfO54c3XAAAAB8SURBVH5G+wbfzutklcQTt1AeEVTd8bWnSoOnjr/Z8bcGosKjpL6PYFC0/aQey9I5NUhtX8D0PJK5DkFRdTt+26J/b9v8N2Lwbc8Y+rZn/7pHTLwWfJ4dn/HRwcpv9CF3vuavyzCeDAbiU8dTB/wtB5CnjqeOp47/fx3/HyHGs0UxeTTeAAAAAElFTkSuQmCC"
    },
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"
    }
   },
   "cell_type": "markdown",
   "id": "d66888ae",
   "metadata": {},
   "source": [
    "## 数据分析咖哥十话\n",
    "\n",
    "## 第2话 远近高低各不同：聚类实现RMF细分\n",
    "\n",
    "**题解** 古有四库全书。所谓“四库”，即经、史、子、集四部，其下又细分为四十四类，这样做的目的是使浩如烟海的典籍易于查阅。对于数据分析师而言，分类分层，将复杂的用户统计数据和购买行为转化成清晰、明确、容易理解的结构，是精准获客和个性化运营的前提。\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "<center>RFM分析的流程</center>\n",
    "\n",
    "**详细内容请参考拙作：《数据分析咖哥十话》** 人民邮电出版社2022年出版\n",
    "![image-3.png](attachment:image-3.png)\n",
    "购书链接：https://item.jd.com/13335199.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "de75f28f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单号</th>\n",
       "      <th>产品码</th>\n",
       "      <th>消费日期</th>\n",
       "      <th>产品说明</th>\n",
       "      <th>数量</th>\n",
       "      <th>单价</th>\n",
       "      <th>用户码</th>\n",
       "      <th>城市</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536374</td>\n",
       "      <td>21258</td>\n",
       "      <td>6/1/2022 9:09</td>\n",
       "      <td>绿联usb分线器 一拖四</td>\n",
       "      <td>32</td>\n",
       "      <td>10.95</td>\n",
       "      <td>15100</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536376</td>\n",
       "      <td>22114</td>\n",
       "      <td>6/1/2022 9:32</td>\n",
       "      <td>加大男装T恤男大码胖子宽松卡</td>\n",
       "      <td>48</td>\n",
       "      <td>50.45</td>\n",
       "      <td>15291</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536376</td>\n",
       "      <td>21733</td>\n",
       "      <td>6/1/2022 9:32</td>\n",
       "      <td>唐装男夏季青年棉麻中国风加肥</td>\n",
       "      <td>64</td>\n",
       "      <td>86.55</td>\n",
       "      <td>15291</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536378</td>\n",
       "      <td>22386</td>\n",
       "      <td>6/1/2022 9:37</td>\n",
       "      <td>越南进口白心火龙果4个装</td>\n",
       "      <td>10</td>\n",
       "      <td>108.95</td>\n",
       "      <td>14688</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536378</td>\n",
       "      <td>85099C</td>\n",
       "      <td>6/1/2022 9:37</td>\n",
       "      <td>大连美早樱桃400g 果径约26mm</td>\n",
       "      <td>10</td>\n",
       "      <td>166.95</td>\n",
       "      <td>14688</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      订单号     产品码           消费日期                产品说明  数量      单价    用户码  城市\n",
       "0  536374   21258  6/1/2022 9:09        绿联usb分线器 一拖四  32   10.95  15100  北京\n",
       "1  536376   22114  6/1/2022 9:32      加大男装T恤男大码胖子宽松卡  48   50.45  15291  上海\n",
       "2  536376   21733  6/1/2022 9:32      唐装男夏季青年棉麻中国风加肥  64   86.55  15291  上海\n",
       "3  536378   22386  6/1/2022 9:37        越南进口白心火龙果4个装  10  108.95  14688  北京\n",
       "4  536378  85099C  6/1/2022 9:37  大连美早樱桃400g 果径约26mm  10  166.95  14688  北京"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np # 导入NumPy\n",
    "import pandas as pd # 导入Pandas\n",
    "import matplotlib.pyplot as plt # 导入Matplotlib 的pyplot 模块\n",
    "import seaborn as sns # 导入Seaborn\n",
    "plt.rcParams[\"font.family\"]=['SimHei'] #用来设定字体样式\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来设定无衬线字体样式\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "df_sales = pd.read_csv('电商历史订单.csv') # 载入数据集\n",
    "df_sales.head() # 输出前几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02721dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>数量</th>\n",
       "      <th>单价</th>\n",
       "      <th>用户码</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>87180.000000</td>\n",
       "      <td>87180.000000</td>\n",
       "      <td>87180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.008167</td>\n",
       "      <td>3.604724</td>\n",
       "      <td>15338.447855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>48.769817</td>\n",
       "      <td>133.475409</td>\n",
       "      <td>392.000551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-9360.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14681.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>15022.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>15334.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>15674.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3114.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>16019.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 数量            单价           用户码\n",
       "count  87180.000000  87180.000000  87180.000000\n",
       "mean      10.008167      3.604724  15338.447855\n",
       "std       48.769817    133.475409    392.000551\n",
       "min    -9360.000000      0.000000  14681.000000\n",
       "25%        2.000000      1.250000  15022.000000\n",
       "50%        4.000000      1.950000  15334.000000\n",
       "75%       12.000000      3.750000  15674.000000\n",
       "max     3114.000000  38970.000000  16019.000000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales.describe() #df_sales 的统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2b383ea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sales = df_sales.loc[df_sales['数量'] > 0] # 清洗掉“数量”小于0 的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "67ee71c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单号</th>\n",
       "      <th>产品码</th>\n",
       "      <th>消费日期</th>\n",
       "      <th>产品说明</th>\n",
       "      <th>数量</th>\n",
       "      <th>单价</th>\n",
       "      <th>用户码</th>\n",
       "      <th>城市</th>\n",
       "      <th>总价</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536374</td>\n",
       "      <td>21258</td>\n",
       "      <td>6/1/2022 9:09</td>\n",
       "      <td>绿联usb分线器 一拖四</td>\n",
       "      <td>32</td>\n",
       "      <td>10.95</td>\n",
       "      <td>15100</td>\n",
       "      <td>北京</td>\n",
       "      <td>350.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536376</td>\n",
       "      <td>22114</td>\n",
       "      <td>6/1/2022 9:32</td>\n",
       "      <td>加大男装T恤男大码胖子宽松卡</td>\n",
       "      <td>48</td>\n",
       "      <td>50.45</td>\n",
       "      <td>15291</td>\n",
       "      <td>上海</td>\n",
       "      <td>2421.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536376</td>\n",
       "      <td>21733</td>\n",
       "      <td>6/1/2022 9:32</td>\n",
       "      <td>唐装男夏季青年棉麻中国风加肥</td>\n",
       "      <td>64</td>\n",
       "      <td>86.55</td>\n",
       "      <td>15291</td>\n",
       "      <td>上海</td>\n",
       "      <td>5539.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536378</td>\n",
       "      <td>22386</td>\n",
       "      <td>6/1/2022 9:37</td>\n",
       "      <td>越南进口白心火龙果4个装</td>\n",
       "      <td>10</td>\n",
       "      <td>108.95</td>\n",
       "      <td>14688</td>\n",
       "      <td>北京</td>\n",
       "      <td>1089.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536378</td>\n",
       "      <td>85099C</td>\n",
       "      <td>6/1/2022 9:37</td>\n",
       "      <td>大连美早樱桃400g 果径约26mm</td>\n",
       "      <td>10</td>\n",
       "      <td>166.95</td>\n",
       "      <td>14688</td>\n",
       "      <td>北京</td>\n",
       "      <td>1669.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      订单号     产品码           消费日期                产品说明  数量      单价    用户码  城市  \\\n",
       "0  536374   21258  6/1/2022 9:09        绿联usb分线器 一拖四  32   10.95  15100  北京   \n",
       "1  536376   22114  6/1/2022 9:32      加大男装T恤男大码胖子宽松卡  48   50.45  15291  上海   \n",
       "2  536376   21733  6/1/2022 9:32      唐装男夏季青年棉麻中国风加肥  64   86.55  15291  上海   \n",
       "3  536378   22386  6/1/2022 9:37        越南进口白心火龙果4个装  10  108.95  14688  北京   \n",
       "4  536378  85099C  6/1/2022 9:37  大连美早樱桃400g 果径约26mm  10  166.95  14688  北京   \n",
       "\n",
       "       总价  \n",
       "0   350.4  \n",
       "1  2421.6  \n",
       "2  5539.2  \n",
       "3  1089.5  \n",
       "4  1669.5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales['总价'] = df_sales['数量'] * df_sales['单价'] # 计算每单的总价\n",
    "df_sales.head() # 输出前几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8d4dc67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'月销售额'}, xlabel='年,月'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 输出月销售额\n",
    "df_sales['年'] = pd.DatetimeIndex(df_sales['消费日期']).year\n",
    "df_sales['月'] = pd.DatetimeIndex(df_sales['消费日期']).month\n",
    "df_sales.groupby(['年', '月'])['总价'].sum().plot(kind='bar', title = '月销售额')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a0ebe72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'各地销售额'}, xlabel='城市'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 输出地区销售额\n",
    "df_sales.groupby(['城市'])['总价'].sum().plot(kind='bar',title = '各地销售额')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f54cefe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>16015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>16016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>16017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>16018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>16019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>980 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       用户码\n",
       "0    14681\n",
       "1    14682\n",
       "2    14684\n",
       "3    14687\n",
       "4    14688\n",
       "..     ...\n",
       "975  16015\n",
       "976  16016\n",
       "977  16017\n",
       "978  16018\n",
       "979  16019\n",
       "\n",
       "[980 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user = pd.DataFrame(df_sales['用户码'].unique()) # 生成以“用户码”为主键的对象df_user\n",
    "df_user.columns = ['用户码'] # 设定字段名\n",
    "df_user = df_user.sort_values(by='用户码',ascending=True).reset_index(drop=True) # 按“用户码”排序\n",
    "df_user # 输出df_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f6efd0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户码   R值\n",
       "0  14681   70\n",
       "1  14682  187\n",
       "2  14684   25\n",
       "3  14687  106\n",
       "4  14688    7"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales['消费日期'] = pd.to_datetime(df_sales['消费日期']) # 转化日期格式\n",
    "df_recent_buy = df_sales.groupby('用户码'). 消费日期.max().reset_index() # 构建消费日期信息\n",
    "df_recent_buy.columns = ['用户码','最近日期'] # 设定字段名\n",
    "df_recent_buy['R值'] = (df_recent_buy['最近日期'].max() - df_recent_buy['最近日期']).dt.days # 计算最新日期距上次消费日期的天数\n",
    "df_user = pd.merge(df_user, df_recent_buy[['用户码','R值']], on='用户码') # 把上次消费日期距最新日期的天数（R 值）合并至df_user 对象\n",
    "df_user.head() # 输出前几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "38337d71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'新进度分布直方图'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_user['R值'].plot(kind='hist', bins=20, title = '新进度分布直方图') #R值直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a044394",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans # 导入KMeans 模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a415558b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_elbow(df): #定义手肘函数\n",
    "    distance_list = [] #聚质心的距离（损失）\n",
    "    K = range(1,9) #K值范围\n",
    "    for k in K:\n",
    "        kmeans = KMeans(n_clusters=k, max_iter=100) #创建KMeans模型\n",
    "        kmeans = kmeans.fit(df) #拟合模型\n",
    "        distance_list.append(kmeans.inertia_) #创建每个K值的损失\n",
    "    plt.plot(K, distance_list, 'bx-') #绘图\n",
    "    plt.xlabel('k') #X轴\n",
    "    plt.ylabel('距离均方误差') #Y轴\n",
    "    plt.title('k值手肘图') #标题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b6d57352",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\huangj2\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_elbow(df_user[['R值']]) #显示R值聚类K值手肘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "803f9f2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "      <th>R值层级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户码   R值  R值层级\n",
       "0  14681   70     1\n",
       "1  14682  187     0\n",
       "2  14684   25     1\n",
       "3  14687  106     0\n",
       "4  14688    7     1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3) # 设定K =3\n",
    "kmeans.fit(df_user[['R值']]) # 拟合模型\n",
    "df_user['R值层级'] = kmeans.predict(df_user[['R值']]) # 通过聚类模型求出R 值的层级\n",
    "df_user.head() # 输出前几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6390c4f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R值层级</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>178.0</td>\n",
       "      <td>157.162921</td>\n",
       "      <td>37.340870</td>\n",
       "      <td>95.0</td>\n",
       "      <td>126.00</td>\n",
       "      <td>156.5</td>\n",
       "      <td>188.75</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>664.0</td>\n",
       "      <td>32.088855</td>\n",
       "      <td>25.141763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>138.0</td>\n",
       "      <td>298.094203</td>\n",
       "      <td>45.436550</td>\n",
       "      <td>231.0</td>\n",
       "      <td>255.25</td>\n",
       "      <td>292.5</td>\n",
       "      <td>334.50</td>\n",
       "      <td>372.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count        mean        std    min     25%    50%     75%    max\n",
       "R值层级                                                                   \n",
       "0     178.0  157.162921  37.340870   95.0  126.00  156.5  188.75  225.0\n",
       "1     664.0   32.088855  25.141763    0.0   10.00   25.0   50.00   94.0\n",
       "2     138.0  298.094203  45.436550  231.0  255.25  292.5  334.50  372.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user.groupby('R值层级')['R值'].describe() #R 值层级分组统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8d7ec803",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为聚类排序\n",
    "def order_cluster(cluster_name, target_name,df,ascending=False):\n",
    "    new_cluster_name = 'new_' + cluster_name # 新的聚类名称\n",
    "    df_new = df.groupby(cluster_name)[target_name].mean().reset_index() # 按聚类结果分组，创建df_new对象\n",
    "    df_new = df_new.sort_values(by=target_name,ascending=ascending).reset_index(drop=True) # 排序\n",
    "    df_new['index'] = df_new.index # 创建索引字段\n",
    "    df_new = pd.merge(df,df_new[[cluster_name,'index']], on=cluster_name) # 基于聚类名称把df_new 还原为df对象，并添加索引字段\n",
    "    df_new = df_new.drop([cluster_name],axis=1) # 删除聚类名称\n",
    "    df_new = df_new.rename(columns={\"index\":cluster_name}) # 将索引字段重命名为聚类名称字段\n",
    "    return df_new # 返回排序后的df_new 对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bef62963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "      <th>R值层级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户码   R值  R值层级\n",
       "0  14681   70     2\n",
       "1  14682  187     1\n",
       "2  14684   25     2\n",
       "3  14687  106     1\n",
       "4  14688    7     2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user = order_cluster('R值层级', 'R值', df_user, False) # 调用簇排序函数\n",
    "df_user = df_user.sort_values(by='用户码',ascending=True).reset_index(drop=True) # 根据用户码排序\n",
    "df_user.head() # 输出前几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0ba3ad07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R值层级</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>138.0</td>\n",
       "      <td>298.094203</td>\n",
       "      <td>45.436550</td>\n",
       "      <td>231.0</td>\n",
       "      <td>255.25</td>\n",
       "      <td>292.5</td>\n",
       "      <td>334.50</td>\n",
       "      <td>372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>178.0</td>\n",
       "      <td>157.162921</td>\n",
       "      <td>37.340870</td>\n",
       "      <td>95.0</td>\n",
       "      <td>126.00</td>\n",
       "      <td>156.5</td>\n",
       "      <td>188.75</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>664.0</td>\n",
       "      <td>32.088855</td>\n",
       "      <td>25.141763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count        mean        std    min     25%    50%     75%    max\n",
       "R值层级                                                                   \n",
       "0     138.0  298.094203  45.436550  231.0  255.25  292.5  334.50  372.0\n",
       "1     178.0  157.162921  37.340870   95.0  126.00  156.5  188.75  225.0\n",
       "2     664.0   32.088855  25.141763    0.0   10.00   25.0   50.00   94.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user.groupby('R值层级')['R值'].describe() #R 值层级分组统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f13b0055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "      <th>R值层级</th>\n",
       "      <th>F值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户码   R值  R值层级   F值\n",
       "0  14681   70     2    7\n",
       "1  14682  187     1    2\n",
       "2  14684   25     2  421\n",
       "3  14687  106     1   15\n",
       "4  14688    7     2  330"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_frequency = df_sales.groupby('用户码'). 消费日期.count().reset_index() # 计算每个用户的消费次数，构建df_frequency 对象\n",
    "df_frequency.columns = ['用户码','F值'] # 设定字段名称\n",
    "df_user = pd.merge(df_user, df_frequency, on='用户码') # 把消费频率整合至df_user 对象\n",
    "df_user.head() # 输出前几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a352427e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\huangj2\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_elbow(df_user[['F值']]) #显示F值聚类K值手肘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "02a6939d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F值层级</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>718.0</td>\n",
       "      <td>32.401114</td>\n",
       "      <td>24.331637</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202.0</td>\n",
       "      <td>153.178218</td>\n",
       "      <td>49.679950</td>\n",
       "      <td>93.0</td>\n",
       "      <td>114.25</td>\n",
       "      <td>139.5</td>\n",
       "      <td>187.25</td>\n",
       "      <td>278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.0</td>\n",
       "      <td>416.641509</td>\n",
       "      <td>111.945573</td>\n",
       "      <td>286.0</td>\n",
       "      <td>329.00</td>\n",
       "      <td>388.0</td>\n",
       "      <td>498.00</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1295.285714</td>\n",
       "      <td>516.333456</td>\n",
       "      <td>899.0</td>\n",
       "      <td>1013.50</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>1321.50</td>\n",
       "      <td>2379.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count         mean         std    min      25%     50%      75%     max\n",
       "F值层级                                                                         \n",
       "0     718.0    32.401114   24.331637    1.0    12.00    27.0    49.00    92.0\n",
       "1     202.0   153.178218   49.679950   93.0   114.25   139.5   187.25   278.0\n",
       "2      53.0   416.641509  111.945573  286.0   329.00   388.0   498.00   710.0\n",
       "3       7.0  1295.285714  516.333456  899.0  1013.50  1119.0  1321.50  2379.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4) #设定K=4\n",
    "kmeans.fit(df_user[['F值']]) #拟合模型\n",
    "df_user['F值层级'] = kmeans.predict(df_user[['F值']]) #通过聚类模型求出F值的层级\n",
    "df_user = order_cluster('F值层级', 'F值',df_user,True) #调用簇排序函数\n",
    "df_user.groupby('F值层级')['F值'].describe() #F值层级分组统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4cc0c689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "      <th>R值层级</th>\n",
       "      <th>F值</th>\n",
       "      <th>F值层级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>421</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>330</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户码   R值  R值层级   F值  F值层级\n",
       "0  14681   70     2    7     0\n",
       "1  14682  187     1    2     0\n",
       "2  14684   25     2  421     2\n",
       "3  14687  106     1   15     0\n",
       "4  14688    7     2  330     2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user = df_user.sort_values(by='用户码',ascending=True).reset_index(drop=True) # 根据“用户码”排序\n",
    "df_user.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c595f8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "      <th>R值层级</th>\n",
       "      <th>F值</th>\n",
       "      <th>F值层级</th>\n",
       "      <th>M值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>498.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>52.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>421</td>\n",
       "      <td>2</td>\n",
       "      <td>1236.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>628.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>330</td>\n",
       "      <td>2</td>\n",
       "      <td>18859.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户码   R值  R值层级   F值  F值层级        M值\n",
       "0  14681   70     2    7     0    498.95\n",
       "1  14682  187     1    2     0     52.00\n",
       "2  14684   25     2  421     2   1236.28\n",
       "3  14687  106     1   15     0    628.38\n",
       "4  14688    7     2  330     2  18859.37"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_revenue = df_sales.groupby('用户码'). 总价.sum().reset_index() # 根据消费金额构建df_revenue 对象\n",
    "df_revenue.columns = ['用户码','M值'] # 设定字段名称\n",
    "df_user = pd.merge(df_user, df_revenue, on='用户码') # 把消费金额整合至df_user 对象\n",
    "df_user.head() # 输出前几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "32131de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([667., 166.,  60.,  27.,  16.,   8.,   9.,   1.,   5.,   3.,   3.,\n",
       "          1.,   1.,   2.,   0.,   2.,   2.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,   1.,   0.,   1.,\n",
       "          0.,   0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          1.,   0.,   1.,   0.,   0.,   1.]),\n",
       " array([6.2000000e+00, 1.2214340e+03, 2.4366680e+03, 3.6519020e+03,\n",
       "        4.8671360e+03, 6.0823700e+03, 7.2976040e+03, 8.5128380e+03,\n",
       "        9.7280720e+03, 1.0943306e+04, 1.2158540e+04, 1.3373774e+04,\n",
       "        1.4589008e+04, 1.5804242e+04, 1.7019476e+04, 1.8234710e+04,\n",
       "        1.9449944e+04, 2.0665178e+04, 2.1880412e+04, 2.3095646e+04,\n",
       "        2.4310880e+04, 2.5526114e+04, 2.6741348e+04, 2.7956582e+04,\n",
       "        2.9171816e+04, 3.0387050e+04, 3.1602284e+04, 3.2817518e+04,\n",
       "        3.4032752e+04, 3.5247986e+04, 3.6463220e+04, 3.7678454e+04,\n",
       "        3.8893688e+04, 4.0108922e+04, 4.1324156e+04, 4.2539390e+04,\n",
       "        4.3754624e+04, 4.4969858e+04, 4.6185092e+04, 4.7400326e+04,\n",
       "        4.8615560e+04, 4.9830794e+04, 5.1046028e+04, 5.2261262e+04,\n",
       "        5.3476496e+04, 5.4691730e+04, 5.5906964e+04, 5.7122198e+04,\n",
       "        5.8337432e+04, 5.9552666e+04, 6.0767900e+04]),\n",
       " <BarContainer object of 50 artists>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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78fo0jaWqHr623QX6x8bod73fjy8B3wMCvMJ0/vcBcAJ4OcnT9JaL5oBX1n0cVTU1P/TWcP+F3lXkde/ndnsep+YYHEsLY2htLBthHH6cX5IaME1r5pKkAQxzSWqAYS5JDTDMJakBhrkkNeD/ATRah/LoiBY/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_user['M值'],bins=50) #M 值直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d04c5f2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\huangj2\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_elbow(df_user[['M值']]) # 输出M 值聚类K 值手肘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "959390a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M值层级</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>834.0</td>\n",
       "      <td>707.683562</td>\n",
       "      <td>581.195733</td>\n",
       "      <td>6.20</td>\n",
       "      <td>263.8175</td>\n",
       "      <td>499.135</td>\n",
       "      <td>1046.4125</td>\n",
       "      <td>2446.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>119.0</td>\n",
       "      <td>4176.392437</td>\n",
       "      <td>1576.679527</td>\n",
       "      <td>2465.48</td>\n",
       "      <td>2875.6300</td>\n",
       "      <td>3672.860</td>\n",
       "      <td>4956.9700</td>\n",
       "      <td>8347.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>13632.917000</td>\n",
       "      <td>3578.349455</td>\n",
       "      <td>9585.91</td>\n",
       "      <td>10627.3100</td>\n",
       "      <td>12454.680</td>\n",
       "      <td>16265.9350</td>\n",
       "      <td>20261.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>46682.748571</td>\n",
       "      <td>10514.233536</td>\n",
       "      <td>33643.08</td>\n",
       "      <td>38523.5500</td>\n",
       "      <td>44534.300</td>\n",
       "      <td>55393.4300</td>\n",
       "      <td>60767.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count          mean           std       min         25%        50%  \\\n",
       "M值层级                                                                       \n",
       "0     834.0    707.683562    581.195733      6.20    263.8175    499.135   \n",
       "1     119.0   4176.392437   1576.679527   2465.48   2875.6300   3672.860   \n",
       "2      20.0  13632.917000   3578.349455   9585.91  10627.3100  12454.680   \n",
       "3       7.0  46682.748571  10514.233536  33643.08  38523.5500  44534.300   \n",
       "\n",
       "             75%      max  \n",
       "M值层级                       \n",
       "0      1046.4125   2446.6  \n",
       "1      4956.9700   8347.2  \n",
       "2     16265.9350  20261.1  \n",
       "3     55393.4300  60767.9  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4) # 设定K =4\n",
    "kmeans.fit(df_user[['M值']]) # 拟合模型\n",
    "df_user['M值层级'] = kmeans.predict(df_user[['M值']]) # 通过聚类模型求出M 值的层级\n",
    "df_user = order_cluster('M值层级', 'M值',df_user,True) # 调用簇排序函数\n",
    "df_user.groupby('M值层级')['M值'].describe() #M 值层级分组统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "099a2164",
   "metadata": {},
   "outputs": [
    {
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       "     用户码   R值  R值层级   F值  F值层级        M值  M值层级\n",
       "0  14681   70     2    7     0    498.95     0\n",
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       "4  14688    7     2  330     2  18859.37     2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user = df_user.sort_values(by='用户码',ascending=True).reset_index(drop=True) # 根据“用户码”排序\n",
    "df_user.head() # 输出前几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dd014cf7",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       用户码   R值  R值层级   F值  F值层级        M值  M值层级  总分\n",
       "0    14681   70     2    7     0    498.95     0   2\n",
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       "2    14684   25     2  421     2   1236.28     0   4\n",
       "3    14687  106     1   15     0    628.38     0   1\n",
       "4    14688    7     2  330     2  18859.37     2   6\n",
       "..     ...  ...   ...  ...   ...       ...   ...  ..\n",
       "975  16015    3     2  182     1    705.39     0   3\n",
       "976  16016    2     2  235     1   1508.76     0   3\n",
       "977  16017   46     2   32     0    211.88     0   2\n",
       "978  16018   38     2   28     0    408.90     0   2\n",
       "979  16019   46     2  160     1   3786.24     1   4\n",
       "\n",
       "[980 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user['总分'] = df_user['R值层级'] + df_user['F值层级'] + df_user['M值层级'] # 求出每个用户RFM 总分\n",
    "df_user # 输出df_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b405b53b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\huangj2\\AppData\\Local\\Temp\\ipykernel_10260\\3690960070.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_user.query(\"总分 < 2 & 总分 > 0\")['总体价值'] = '低价值'\n",
      "C:\\Users\\huangj2\\AppData\\Local\\Temp\\ipykernel_10260\\3690960070.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_user.query(\"总分 < 4 & 总分 > 2\")['总体价值'] = '中价值'\n",
      "C:\\Users\\huangj2\\AppData\\Local\\Temp\\ipykernel_10260\\3690960070.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_user.query(\"总分 < 6 & 总分 > 4\")['总体价值'] = '高价值'\n"
     ]
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       "<p>980 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       用户码   R值  R值层级   F值  F值层级        M值  M值层级  总分\n",
       "0    14681   70     2    7     0    498.95     0   2\n",
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       "2    14684   25     2  421     2   1236.28     0   4\n",
       "3    14687  106     1   15     0    628.38     0   1\n",
       "4    14688    7     2  330     2  18859.37     2   6\n",
       "..     ...  ...   ...  ...   ...       ...   ...  ..\n",
       "975  16015    3     2  182     1    705.39     0   3\n",
       "976  16016    2     2  235     1   1508.76     0   3\n",
       "977  16017   46     2   32     0    211.88     0   2\n",
       "978  16018   38     2   28     0    408.90     0   2\n",
       "979  16019   46     2  160     1   3786.24     1   4\n",
       "\n",
       "[980 rows x 8 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这是不成功的方法\n",
    "df_user.query(\"总分 < 2 & 总分 > 0\")['总体价值'] = '低价值'\n",
    "df_user.query(\"总分 < 4 & 总分 > 2\")['总体价值'] = '中价值'\n",
    "df_user.query(\"总分 < 6 & 总分 > 4\")['总体价值'] = '高价值'\n",
    "df_user # 输出df_user 对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "722b0c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>498.95</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>低价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>52.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>低价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>421</td>\n",
       "      <td>2</td>\n",
       "      <td>1236.28</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>中价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>628.38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>低价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>330</td>\n",
       "      <td>2</td>\n",
       "      <td>18859.37</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>高价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>16015</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
       "      <td>705.39</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>中价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>16016</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>235</td>\n",
       "      <td>1</td>\n",
       "      <td>1508.76</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>中价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>16017</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>211.88</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>低价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>16018</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>408.90</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>低价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>16019</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>3786.24</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>中价值</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>980 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       用户码   R值  R值层级   F值  F值层级        M值  M值层级  总分 总体价值\n",
       "0    14681   70     2    7     0    498.95     0   2  低价值\n",
       "1    14682  187     1    2     0     52.00     0   1  低价值\n",
       "2    14684   25     2  421     2   1236.28     0   4  中价值\n",
       "3    14687  106     1   15     0    628.38     0   1  低价值\n",
       "4    14688    7     2  330     2  18859.37     2   6  高价值\n",
       "..     ...  ...   ...  ...   ...       ...   ...  ..  ...\n",
       "975  16015    3     2  182     1    705.39     0   3  中价值\n",
       "976  16016    2     2  235     1   1508.76     0   3  中价值\n",
       "977  16017   46     2   32     0    211.88     0   2  低价值\n",
       "978  16018   38     2   28     0    408.90     0   2  低价值\n",
       "979  16019   46     2  160     1   3786.24     1   4  中价值\n",
       "\n",
       "[980 rows x 9 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在df_user 对象中添加“总体价值”字段\n",
    "df_user.loc[(df_user['总分']<=2) & (df_user['总分']>=0), '总体价值'] = '低价值'\n",
    "df_user.loc[(df_user['总分']<=4) & (df_user['总分']>=3), '总体价值'] = '中价值'\n",
    "df_user.loc[(df_user['总分']<=8) & (df_user['总分']>=5), '总体价值'] = '高价值'\n",
    "df_user # 输出df_user 对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fe9a7e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2019a6f99d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 输出高、中、低价值用户的分布散点图（F 值与M 值）\n",
    "plt.scatter(df_user.query(\"总体价值 == '高价值'\")['F值'], # 五角星代表高价值用户\n",
    "df_user.query(\"总体价值 == '高价值'\")['M值'],c='g',marker='*')\n",
    "plt.scatter(df_user.query(\"总体价值 == '中价值'\")['F值'], # 圆点代表中价值用户\n",
    "df_user.query(\"总体价值 == '中价值'\")['M值'],marker=8)\n",
    "plt.scatter(df_user.query(\"总体价值 == '低价值'\")['F值'], # 三角形代表低价值用户\n",
    "df_user.query(\"总体价值 == '低价值'\")['M值'],c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "155c3b87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6)) # 图片大小\n",
    "ax = plt.subplot(111, projection='3d') # 坐标系\n",
    "ax.scatter(df_user.query(\"总体价值 == '高价值'\")['R值'], # 散点图\n",
    "df_user.query(\"总体价值 == '高价值'\")['F值'],\n",
    "df_user.query(\"总体价值 == '高价值'\")['M值'], c='g',marker='*')\n",
    "ax.scatter(df_user.query(\"总体价值 == '中价值'\")['R值'],\n",
    "df_user.query(\"总体价值 == '中价值'\")['F值'],\n",
    "df_user.query(\"总体价值 == '中价值'\")['M值'], marker=8)\n",
    "ax.scatter(df_user.query(\"总体价值 == '低价值'\")['R值'],\n",
    "df_user.query(\"总体价值 == '低价值'\")['F值'],\n",
    "df_user.query(\"总体价值 == '低价值'\")['M值'], c='r')\n",
    "ax.set_xlabel('R值') # 坐标轴\n",
    "ax.set_ylabel('F值') # 坐标轴\n",
    "ax.set_zlabel('M值') # 坐标轴\n",
    "plt.show() # 输出"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4b8b550",
   "metadata": {},
   "source": [
    "总结到这里，马总突然发问：“咖哥，咱们快看看我这些用户中，哪些是最有价值的。我们要把他们列为VIP用户。我要为他们赠送专属礼包，邀请他们参与新一轮的推广活动，他们的朋友们一定也会成为新的VIP。”\n",
    "咖哥说：“那很简单，就输出最高分值得的用户的姓名就好啦。”\n",
    "说时迟，那是快，咖哥迅速的写了一行代码，输出了总分为8分的用户。\n",
    "\n",
    "df_user.query(\"总分 == 8\")\n",
    " \n",
    "大家发现总分为8的用户只有一个人，这个用户的R、F、M三个值都是最高层级，显然是VIP中的VIP。\n",
    "马总赶忙从他的手机App中查询该编号为15311号用户的具体信息，还真给查着了。\n",
    " \n",
    "罗小雪居然是VIP用户！\n",
    "不料，查询的结果令大家大吃一惊，赫然是看到了小雪的名字！\n",
    "“重名了吧！”小雪哈哈笑到。“我希望我男朋友在这个列表里面倒是真的呢。”\n",
    "\n",
    "**详细内容请参考拙作：《数据分析咖哥十话》** 人民邮电出版社2022年出版"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8b15c2f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
